Macroeconomic Variables as Predictors of US …...equity prices, with industrial production relates to real stock returns in an increasingly important manner over time, as compared

Macroeconomic Variables as Predictors of US Equity Returns

By: Raphael Doyon (260763986)

Kevin Yulianto (260768885)

Master of Management in Finance

McGill University

2018

Abstract

A significant amount of literature investigating the relationship between equity returns and

macroeconomic variables has been produced since the 1970’s. While some studies confirmed

the relationship between certain variables and equity returns in the U.S. equity market, many

others give contradicting results. Disparity in findings also exists regarding the cause and effect

relationship between macroeconomic variables and equity returns. The purpose of this study is

to conclude whether macroeconomic variables can explain real equity returns in the U.S. equity

market. Macroeconomic variables considered in this paper include those studied previously, and

also the so-called leading indicators, such as consumer confidence and housing starts. The

significance of macroeconomic variables in explaining equity returns is measured using

multivariate linear regressions. Contrary to previous researches, this study found a significant

relationship between annual real equity returns and the change in consumer confidence and in

housing starts. A relationship is also found between real equity returns and the risk premium, as

measured by the spread between the yields on BBB corporate bonds and U.S. Treasury bonds.

No significant relationship between current real equity returns and future industrial production was

found. However, using monthly real equity returns, 1-month lead consumer confidence, 6-month

lead change in housing starts and 11-month lag money supply growth are found to be significant.

Finally, using those variables and testing both annual and monthly models in out-of-sample data,

the result supports the argument that macroeconomic variables do have power in explaining real

equity returns.

Keywords: Economic Variables, Stock Return, United States

Table of Contents

Abstract i

Table of Contents ii

Chapter I Introduction 1

1.1 Research Background 1

1.2 Problem Statement 2

1.3 Research Objectives 3

Chapter II Literature Review 4

2.1 Macroeconomic Variables and Equity Returns 4

2.2 Nominal Economic Variables and Equity Returns 6

2.3 Real Economic Variables and Equity Returns 7

2.4 Cause and Effect Relationship Between Macroeconomic Variables

and Equity Returns 10

2.5 Theoretical Framework 11

Chapter III Research Methodology 12

3.1 Data and Data Sources 12

3.2 Research Design 14

3.3 Research Hypotheses 24

Chapter IV Findings and Discussion 25

4.1 Results 25

4.2 Discussion 31

Chapter V Conclusion 36

References 38

1

Chapter I

Introduction

1.1 Research Background

1.1.1 A Simple Model of Equity Value

In doing valuation of equity securities, the equity value contains expectation regarding

future cash flows, the growth rate of those cash flows, and their riskiness. Riskiness of

the security is reflected through the discount rate in the Gordon Growth Model and is

dependent on the non-diversifiable risk that investors set in market equilibrium.

𝑃𝑡 = ∑𝐷𝑖

(1 + 𝑝)𝑖

∞

𝑖=1

In the present value model presented above, Pt represents the stock price at time t, p is

a discount rate that includes a risk premium that compensates investors for holding on to

the risky asset, and Di is the dividend paid at time i. Hence, the determination of the

current price depends solely on the future dividends, the risk-free rate, and the risk

premium associated with holding that specific security.

1.1.2 Macroeconomic variables and Equity Value

Macroeconomic variables affect certain drivers of equity value presented above. Two of

the most common macroeconomic variables used to proxy for future dividends and the

risk premium of equities are industrial production and interest rates, respectively. An

increase (decrease) in industrial production is proposed to affect stock price in the same

direction, through an increase (decrease) in the expected future dividends. In contrast,

2

changes in interest rates affect equity prices in the opposite direction, as it increases the

denominator value in the Gordon Growth Model.

Changes in interest rates are hypothesized to affect equity prices in two different ways.

First, a change in the interest rates affects equity value directly, through the increase in

discount rates, and a change in interest rate also indirectly impact the equity value through

changes in future production, which then influence future dividends in the numerator of

Gordon Growth Model. Higher interest rates decrease investment and future production

level, which then translates to lower dividend payment in the long run (Peiro, 2016).

Empirically this is proven by study that concludes equity prices in the US are positively

impacted by future variation in industrial production and negatively by current changes in

interest rates (Peiro, 1996).

Macroeconomic news therefore, can be representative of the risk factors to firm’s cash

flows. Economic data partly reflects the prevalent economic environment in which firms

operates, which influences the availability of investment opportunities and future cash

flows (Chen, Roll, and Ross, 1986; Flannery and Protopapadakis, 2002).

Early studies support the argument that macroeconomic variables influence the risk

premium required by investors in determining the discount rate for a security, hence it

could be considered as a proxy for pervasive risk factors in the market (Chen, Roll, and

Ross, 1986; Priestley, 1996; Kryzanowski et al, 1997). However, later studies show

conflicting results that question the evidence of whether equity returns are influenced by

macroeconomic developments (Chan, Karcesky, and Lakonishok, 1998; Flannery and

Protopapadakis, 2002). There seems to be no existing consensus on whether equity

returns can be explained by macroeconomic variables.

1.2 Problem Statements

There is a gap in understanding how various macroeconomic variables affects stock

returns, and whether current stock returns also affect economic conditions in the future.

Industrial production growth and long-term interest rates have long been documented to

significantly affect stock returns, despite the feedback loop relationship may be involved

3

with these variables and stock returns. Nevertheless, the impacts of macroeconomic

developments on stock returns and the direction of the relationship linking them have

been unclear and even contradictive across studies. In short, this research seeks to

address the following questions:

1. Is there a relationship between change in industrial production, housing starts,

or consumer confidence and real equity returns?

2. Is there a relationship between long-term real interest rate, risk premium, or term

structure and real equity returns?

3. Is there a relationship between change in money supply (M2) and real equity

returns?

4. Is there a relationship between real equity returns in and real economic activity?

1.3 Research Objectives

This research seeks to find evidence and explanation regarding the relationship between

various macroeconomic variables and real equity returns in the US stock market. More

specifically, this research seeks to find the answer of whether real equity returns predict

future industrial production, or industrial production growth can be used to predict real

equity returns. The results could offer better clarity on contradictive findings concerning

the directions and relationship between macroeconomics variables and equity returns. In

addition to that, this research further tests if the relationship that was claimed to hold

between certain macroeconomic variables and equity returns in the past still holds when

tested using longer sample period (1972-2013). Finally, this paper also seeks to find out

whether certain less documented macroeconomic variables, such as the change in

housing starts, contributes in explaining variation of real equity returns.

4

Chapter II

Literature Review

2.1 Macroeconomic Variables and Equity Returns

Since 1960, researches have been done to find macroeconomics variables that could

help predict equity returns. The idea was to explain how economic activity or production

could be translated into macroeconomic data, and how it could affect prices in the equity

market. Money supply is a variable that was commonly used in studies in explaining stock

returns, changes in money supply affect the equilibrium position of money in the market,

thereby changing the prices of securities and the composition of the investor’s portfolio

(Cooper, 1974).

Changes in money supply are also hypothesized to affect real economic variables, such

as employment, trade balance, and housing starts, which then have an indirect effect on

future equity market returns (Rogalski and Vinso, 1977). These direct and indirect impacts

suggest that an increase in money supply has a positive effect on equity market returns.

Following this hypothesis, researches have been done to seek the evidence of different

macroeconomics variables that influence equity market returns.

Chen, Roll and Ross (1986) are amongst the pioneers who tried to answer whether

certain macroeconomic variables could serve as a proxy for risks factors that reward

investors in the equity market. They found that macroeconomic variables, such as the

term spread between long-term and short-term interest rates, the expected and

unexpected inflation, the industrial production, and the spread between high and low-

grade bonds, do reward investors in the US equity market.

The use of term spread by Chen, Roll and Ross (1986) had also been found to explain

stock and bond returns in a study by Keim and Stambaugh (1986). Furthermore, Fama

and French (1989) linked the cyclicality in expected stock returns to the term spread,

arguing that a high spread referred to a business cycle through while a low spread

referred to a peak in the cycle.

5

Chen, Roll, and Ross (1986) also included a default spread (the difference between

corporate bond yield and government bond yield) in their analysis, to serve as a proxy for

business conditions that affect equity returns. They argue that this spread is high during

poor economic conditions, as investors shy away from assets of riskier firms and opt for

safe-haven securities such as government bond, while the spread is low during good

economic condition, when investors are less worried in holding risky assets. On a

subsequent study, Chen (1989) proves that the default spread has a negative correlation

with past and future output growth, making it a good variable to represent business

conditions that affects expected equity returns.

Bilson, Brailsford, and Hooper (2000) conducted a study focusing on emerging markets

equities and found that equity returns in their sample were significantly related to the

lagged money supply and the exchange rate but are weakly related to goods prices or

real activity.

Study done in the US stock market (Humpe and Macmillan, 2009) concludes that equity

prices are positively affected by industrial production and negatively by long-term interest

rates as well as the consumer price index. More recently, Peiro (2016) uses an updated

sample period in the European market, in an effort to find the dependence of equity

returns on macroeconomic variables in the French, German, and the British markets. His

findings are similar to those of Humpe and Macmilan (2009), in which he found industrial

production and long-term interest rates are two variables having an important explanatory

power. Together, those two variables account for about one-half of annual variations in

equity prices, with industrial production relates to real stock returns in an increasingly

important manner over time, as compared to interest rates.

After establishing empirical fact regarding relationship between economic variables and

equity returns, researches began questioning whether the sequence of economic data

announcement affect the relative impact to equity returns. With this regard, Flannery and

Protopapadakis (2002) look at economic variables announcements made at the

beginning of the month and compare the impact of those announcements on equity prices

with the impact of announcements made later in the month. They conclude that the

6

sequence of announcement of macroeconomic variables is not as important as the

macroeconomic variables themselves in affecting equity returns.

2.2 Nominal Economic Variables and Equity Returns

Early papers discussing the relationship between equity returns and macroeconomic

variables focuses on the use of variables often labelled nominal economic variables.

Those nominal variables include money supply, inflation rate and the level of interest

rates, usually proxied by nominal bond yields.

Initial research by Fama and Schwert (1977) found negative relationship between inflation

and nominal stock returns. However, that study was not able to conclude on the causality

between inflation and return. Further researches on the topic show that inflation and

money supply growth are negatively related to stock returns, with the rational that higher

money supply will trigger inflation, which then forces central bank to raise interest rates

that is detrimental to equity returns (Flannery and Protopapadakis, 2002; Peace and

Roley, 1985; Bodie, 1976).

Decades after the initial study on the topic, Chan, Karceski, and Lakonishok (1998) refute

the argument that macroeconomics factors affect equity returns, on the basis that any

relationship found to be statistically significant in previous studies was simply due to

randomly generated series of numbers that were picking up covariation in returns.

The contradictive conclusion reached by authors who worked on the relationship between

equity returns and inflation makes it difficult to conclude on whether stocks can effectively

protect invested capital from the eroding effect of inflation. Nevertheless, equities are

commonly theorized to be an effective hedge against inflation. Thinking of it from a capital

structure perspective, equity security is a residual claim on the nominal assets of the firm.

Thus, it is a residual claim on the cash and cash equivalent as well as on the real assets

of the firm. The inflation hedge property is claimed to exist because in the presence of

inflationary pressure, an increase in the value of the real assets of the firm should also

translate into a higher value for a claim on the residual asset of that firm. However, it is

7

important to note that this inflation-hedge property gets weaker apart when a firm holds a

substantial amount of cash balance, receivables, or fixed income securities.

If we exclude financial and utilities firms, the median cash ratio of US firms was 13.3% in

2006, a significant increase since from 5.5% in 1980. Kahle, and Stulz (2009) found that

firms now have less receivables and more cash on hand. In addition to that, they also

concluded that the cash flows of the firms were more volatile, and firms were spending

more on research and development in 2006 than they were in 1980. Given the higher

cash flow volatilities in 2006, one can argue that firms keep more cash on hand to fund

their research and development activities, in order to avoid having to cut on research and

development during economic downturn. By doing so, they should be even more exposed

to inflation, which increase the importance of investigating whether stocks are a good

hedge against inflation.

If we assume that investors price financial assets in real terms, i.e., considering the

erosive impact of inflation on their future spending abilities, then we could conclude that

inflation affects equity market returns. In fact, Chen, Roll and Ross (1986) found that both

expected and unexpected inflation can helps explain variations in equity returns.

In the Gordon Growth Model discussed earlier, the discount rate employed to discount

dividends has two components, the risk-free rate and the risk premium, which both can

be derived from nominal government and corporate bond yields. Apart from the level of

interest rate itself, it was found that the slope of the yield curve also matters in pricing

equity value. Risk premium in this model refers to the one present in the fixed income

market that corresponds to the additional return required to hold a risky corporate debt as

opposed to holding a risk-free government debt (Chen, Roll and Ross, 1986).

2.3 Real Economic Variables and Equity Returns

Real economic variables are also frequently used in academic research to explain equity

returns. One of the first such real economic variable used to explain equity returns is the

industrial production. Fama (1990) shows industrial production could explain more of the

8

equity return variation than other real variables such as the growth rates of the Gross

National Product or the Gross Private Investment.

The level of production in the economy is hypothesized to correlate positively with higher

cash flows generated by the firm. Indeed, in a booming economy where production is

rising, the increase in production is commonly associated with an increase in the

profitability of the firm, resulting in higher expected cash flows for investors. Assuming

stock prices reflect investor’s expectation of future cash flows in the future, it means that

the change in stock prices partially reflects the level of industrial production investors are

expecting in the coming months (Chen, Roll, Ross, 1986). Cutler, Poterba and Summers

(1989) also found that industrial production growth was significantly and positively

correlated with real equity returns. This relationship also holds in the European market,

where study done by Canova and De Nicolo (1995) shows that equity returns are found

correlate significantly to industrial production level.

However, even though some models of real macroeconomic variables were found to

explain some variations in equity returns, the R-squared of those models are commonly

very low, implying it could only explain a small fraction of variation in equity market

returns. Therefore, one could not rely on such model for predictions (Roll, 1988).

McQueen and Roley (1993) argued that this low R-Square comes from the fact that

economic data surprises have different implications for equities in different stage of the

business cycle. Therefore, they claimed few variables could explain equity returns in a

consistent manner across the business cycle. They found that using a model with

constant coefficients, only two out of the eight macroeconomics variables considered are

significant in explaining returns on the S&P500. One of those two variable is the month-

on-month growth in industrial production. When considering a model that varies in

different economic regimes, they found that six out of the eight macroeconomic variables

considered became significant in explaining market returns.

The argument relating to economic data surprise having different implications in different

economic regimes is also supported by Boyd, Jagannathan and Hu (2001). Their

research shows surprisingly high unemployment rate have a positive impact on equity

returns during economic expansion but a negative one during economic contraction.

9

Therefore, the nature of relationship between level of employment and equity returns is

complex and less agreed upon. On one hand, an increase in employment typically depicts

an improving economic environment, which tend to be accompanied by positive equity

returns. However, as the employment level increase, it can also be followed a rise the

inflation rate, which can trigger monetary policy tightening. As a result, an environment of

increasing interest rate translates into lower equity returns (Peiro, 2016).

To investigate whether macroeconomic variables could be implemented to earn excess

return in the stock market, Lamont (2000) considered portfolios that tracked real variables

such as the growth rate of the industrial production, consumption and labor income. The

result of that study is that portfolios could generate abnormal positive returns by using

signal from some of these real indicators. However, it was found that portfolios tracking

the growth in the Consumer Price Index, i.e, portfolios tracking the inflation rate, could not

generate abnormal positive returns.

Another decent variable that helps explain equity returns is consumer confidence, often

proxied by the University of Michigan consumer confidence index (CCI). Otoo (1999)

found that returns of the Wilshire 5000 Index are related to future rise in consumer

confidence. Meanwhile, Fisher and Statman (2002) found a statistically significant

relationship between the returns of the S&P500 Index and the change in consumer

confidence.

As a forecasting variable, Lemmon and Portniaguina (2006) found that consumer

confidence holds forecasting power for the returns of small cap stocks in the US market,

but this relationship holds only for the period after 1997. In a subsequent study, Fisher

and Statman (2002) found consumer confidence can help predict future economic activity

but they found no statistically significant relationship when trying to explain the S&P500

Index returns with past consumer confidence data. However, they also note that

consumer confidence tends to move in tandem with equity prices, the relationship

between equity returns and concurrent consumer confidence is statistically significant.

The housing starts figure is also often referred to, alongside the consumer confidence, as

a leading indicator for equity returns. However, the relationship between that variable and

equity returns is not heavily documented. Given it is often paired with consumer

10

confidence, one could theorize it could also have an interesting explanatory power

investigates on whether it could be used as a replacement of consumer confidence in

some model to explain return variations.

2.4 Cause and Effect Relationship Between Macroeconomic Variables and Equity

Returns

The direction of the relationship between macroeconomics variables and equity returns

is a source of debate in the literature. There is strong evidence of a very significant

positive relationship between industrial production and returns of the U.S. equity market.

However, the cause and effect of that relationship is not clear. For instance, some papers

found that models with lags of industrial production could explain current equity returns

(James, Koreisha, and Partch, 1985). On the other hand, other studies arrive to the

opposite conclusion.

Even though the direction of the relationship between the equity market performance and

macroeconomic variables is not yet fully understood, most authors, treat stock market

returns as an endogenous variable that responds to macroeconomic forces. This is in line

with the approach taken by Chen, Roll and Ross (1986) when they first tackled the

problem of explaining equity returns with macroeconomic variables.

Fama (1990) argues that macroeconomic variables should not predict equity returns. His

argument is that stock prices should reflects expected future cash flows. Therefore, as

future cash flow should also relate to production, then stock prices should predict the

future macroeconomic environment. Fama showed the existence of a strong relationship

between real stock returns and the growth rate in industrial production. Those conclusions

also agree with the earlier findings of Fischer and Merton (1984) and recent study by

Peiro (2016), who concludes that equity returns do forecast future industrial production.

In his study, Peiro (2016) found that equity prices predict movements in production one

year ahead and equity prices move concurrently with interest rates. One-half of the

variations in equity returns can be explained by changes in industrial production and

interest rates.

11

2.5 Theoretical Framework

Figure 2.5. Real equity returns and macroeconomic explanatory candidates

12

Chapter III

Research Methodology

3.1 Data and Data Sources

To investigate the relationship between various macroeconomic variables and stock

returns, we use data from the 1972 – 2013 to capture the long-term relationship between

the variables outlined in Table 3.1 and real US equity returns. We consider both monthly

and annual data for our analysis.

Table 3.1 present the variables used in the analysis, the data used to construct each one

of those variable, as well as the sources of those data.

Table 3.1. Data and Variables Summary

Variable Data Data Source

Real Stock Return YoY S&P 500 Total Return Index (SPXT) Bloomberg

US Consumer Price Index, All Items (CPI) US Bureau of Labor Statistics

IP Growth YoY US Industrial Production Index (IP) Federal Reserve

Real Interest Rate US Government Benchmarks, 10 years, USD

(USG10)

Macrobond

US Consumer Price Index, All Items (CPI) US Bureau of Labor Statistics

M2 Growth YoY Money Supply, USD, (M2) US Conference Board

Housing Starts Growth

YoY

US Residential Construction Starts, New

Privately Owned (HS)

US Census Bureau

Consumer Confidence

Growth YoY

Consumer Confidence Index (CCI) US Bureau of Labor Statistics

Risk Premium US Corporate Benchmarks, 10 year, USD,

BBB rated (USC10)

Macrobond

US Government Benchmarks, 10 years, USD

(USG10)

Macrobond

Term Structure US Government Benchmarks, 10 years, USD

(USG10)

Macrobond

US Government Benchmarks, T-Bills,

Secondary Market, 1 Month Yield (USG3m)

Federal Reserve

13

The formulas for constructing each variable is presented below:

1- Inflation rate YoY (𝜋𝑡):

𝜋𝑡 = log (𝐶𝑃𝐼𝑡

𝐶𝑃𝐼𝑡−1)

2- Nominal stock return YoY (𝑟𝑡):

𝑟𝑡 = log (𝑆𝑃𝑋𝑇𝑡

𝑆𝑃𝑋𝑇𝑡−1)

3- Real equity returns YoY (𝑅𝑡):

𝑅𝐸𝑅𝑡 = 𝑟𝑡 − 𝜋𝑡

4- IP Growth YoY (∆𝐼𝑃𝑡):

𝐺𝐼𝑃𝑡 = log (𝐼𝑃𝑡

𝐼𝑃𝑡−1)

5- Real Interest Rate (𝐼𝑅𝑡):

𝐼𝑅𝑡 = 𝑈𝑆𝐺10𝑡 − 𝜋𝑡

6- M2 Growth YoY (∆𝐼𝑃𝑡):

𝑀2𝑡 = log (𝑀2𝑡

𝑀2𝑡−1)

7- Housing Starts Growth YoY (∆𝐻𝑆)

𝐷𝐻𝑆𝑡 = log (𝐻𝑆𝑡

𝐻𝑆𝑡−1)

8- Consumer Confidence Growth YoY (𝐷𝐶𝐶):

𝐷𝐶𝐶𝑡 = log (𝐶𝐶𝐼𝑡

𝐶𝐶𝐼𝑡−1)

14

9- Risk Premium (𝑅𝑃𝑡):

𝑅𝑃𝑡 = 𝑈𝑆𝐶10𝑡 − 𝑈𝑆𝐺10𝑡

10- Term Structure (𝑇𝑆𝑡):

𝑇𝑆𝑡 = 𝑈𝑆𝐺10𝑡 − 𝑈𝑆𝐺3𝑚𝑡

3.2 Research Design

3.2.1 Research Framework

This paper attempts to provide empirical evidence on US equity returns and

macroeconomic variables with a longer sample period (1972-2013). Most of these

variables have previously been studied and documented in different sample period, but

most of the researches are limited to a narrow sample period, with the exception of study

by Peiro (2016). Our analysis combines variables from some of those studies, but also

includes other ones, less documented, but considered as good leading indicators of

equity returns by practitioners (see table 3.1).

The approach taken in this research follows Peiro (2016) in using real terms for both

macroeconomic variables and equity returns. The motivation to work with real equity

returns is further reinforced by the work of Fama (1990), which highlights the inflation

hedge property exhibited by equities over the 1953-1997 period. Furthermore, one of our

regressor variable is the industrial production, which itself measures production of real

goods in the manufacturing sector. Therefore, working in terms of real equity returns

provides consistency.

Selection of the time interval is also an important factor in analyzing the ability of

macroeconomic variables to explain real equity returns, and this paper use both a monthly

and annual time interval. The reason comes from the goal of the study, which is to

compare the results obtained from monthly and annual returns models with the findings

of previous papers. As an example, when regressing stock returns over macroeconomic

variables, Fama and Kaul (1981) found that when they could only explain 6% of the

variation in monthly returns. However, using annual returns, they obtained a model with

15

a much higher R-squared of 43%. Similarly, Peiro (2016) found out that the he could

explain up to 44% of the variation in real equity returns when using a model with annual

returns while only 14% of the variation in real returns could be explained by using a model

with monthly data. Performing the analysis on both annual and monthly returns also

allows us to compare and possibly contrast the set of variables which provide significant

explanatory power for each model.

3.2.2 Regression Model

To measure how the set of macroeconomics variables presented in table 1 relates to the

real equity returns over our sample period, linear regression model is used. Real equity

returns are treated as the dependent variable and are regressed against macroeconomic

variables, which are used as independent variables in the linear model. Annual and

monthly dataset is used to develop the model that best explain the variation in real equity

returns. The linear regression model has the following form:

𝑅𝐸𝑅�̂� = 𝛼 + 𝛽1𝑋1,𝑖 + 𝛽2𝑋2,𝑖 + . . . + 𝛽𝑛𝑋𝑛,𝑖 + 휀𝑖

Where:

𝑅𝐸𝑅�̂� = 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑟𝑒𝑎𝑙 𝑒𝑞𝑢𝑖𝑡𝑦 𝑟𝑒𝑡𝑢𝑟𝑛𝑠 𝑖𝑛 𝑝𝑒𝑟𝑖𝑜𝑑 𝑖

𝛽𝑖 = 𝑟𝑒𝑔𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑐𝑜𝑟𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑖𝑛𝑔 𝑡𝑜 𝑟𝑒𝑔𝑟𝑒𝑠𝑠𝑜𝑟 𝑋𝑖

휀𝑖 = 𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚 𝑐𝑜𝑟𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑖𝑛𝑔 𝑡𝑜 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑖

3.2.3 Validating for Stationarity using Unit Root Test

To justify the use of a linear regression model, stationarity check of each macroeconomic

variables used as regressors must be done. Unit root test was conducted for each of the

time series variables presented in table 1 using the Augmented Dickey-Fuller (ADF)

method. The null hypothesis Ho states that the time series possesses a unit root, implying

16

the variable is not stationary. If the null hypothesis is rejected for a variable, then the non-

stationarity is rejected, justifying the possible inclusion of that independent variable in the

linear model. In the model using annual real equity returns as the dependent variables,

the long-term real interest rate was found to be non-stationary. As some of the values for

the real long-term interest rate were negative, first differencing long-term real interest rate

gives a figure that is economically hard to interpret. For that reason, it was decided to

drop the long-term real interest rate altogether from the analysis.

3.2.4 Specifying Regression Model Variables

Once stationarity is validated, the next step is to find the variables that affect real equity

returns, keeping in mind that, as found in past academic studies, some macroeconomic

variables could affect stock returns in a leading, concurrent or even lagging time period.

For that reason, it is important to not only find which variables help explain equity returns,

but also, if applicable, determine the lags or leads of those variables. From findings of

past studies and economic rationale, it was decided to limit the range of lags and leads

from a one-year lag to a one-year lead for each macroeconomic variable for the annual

dataset, and for the analysis of monthly returns, the model allowed lags or leads of up to

12 months for each regressor. This is also justified by the design of our data, which

corresponds for many variables in a year-on-year growth rate. To determine the variables,

lags, or/and leads that are linked to real equity returns, we did Pearson correlation

analysis and univariate regression between real equity returns and each macroeconomic

variable to look for variables that is significant at the 5% level. The results allow us to

determine initial candidate for the independent variables on which to regress the real

equity returns.

3.2.5 Best-Subsets Approach

In constructing the multivariate regression model, it is important to keep in mind that each

regressor added to the model may helps explain part of the variation in returns but is not

justified by the addition of complexity in the model, or possibly without an economic

17

justification. To remedy this issue and potentially avoid overfitting the model, the adjusted

R-squared is considered, which penalizes for the number of regressors used in the model.

𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅2 = 1 – [(1 − 𝑟2)𝑛 − 1

𝑛 − 𝑘 − 1]

Where:

𝑟2 =𝑅𝑒𝑔𝑟𝑒𝑠𝑠𝑖𝑜𝑛𝑠 𝑠𝑢𝑚 𝑜𝑓 𝑠𝑞𝑢𝑎𝑟𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑠𝑢𝑚 𝑜𝑓 𝑠𝑞𝑢𝑎𝑟𝑒𝑠

n = sample size

k = number of independent variables in the regression equation

To start, regression between real equity returns over the complete set of independent

variables is conducted. To narrow the number of independent variables in the regression,

variables that are significant at the 5% level in the Pearson correlation table are included.

From the regression result, we then look for the significance of each regressor at the 10%

level. If few regressors are found to be not significant, the least significant one, i.e., the

one with the largest p-value, is dropped from the model. This process is repeated until we

obtain a linear model where all regressors are significant at the 10% level.

As illustrated in figure 2, we use the best-subsets approach to obtain the most efficient

model possible. It deals with the potential multicollinearity between independent variables

and exclude redundant variables (see section 3.2.6 for more on multicollinearity testing).

The efficiency of each linear model is measured by its adjusted R-squared and the

presence of low or no multicollinearity. In addition to that, an F-test, as described below,

is performed to assess the significance of every coefficients in the model simultaneously.

𝐹0 =(𝑆𝑆𝑅𝑟 − 𝑆𝑆𝑆𝑢𝑟) / 𝑞

𝑆𝑆𝑅𝑢𝑟/(𝑛 − (𝑘 + 1))

Where:

18

SSRr = Sum of Squared Residual of the Restricted Model

SSRur = Sum of Squared Residual of the Unrestricted Model

n = Number of Observation

k = Number of Independent Variables in the Unrestricted Model

General hypothesis for F-test:

• H0: b0 = b1 = b2 = b3 = bi = 0 (intercept only model is superior)

• Ha: at least one of bi ≠ 0 (model with predictors is superior)

Validating the significance of the model could be done through the p-value of the F-test.

If that p-value is greater than the desired level of significance ∝, then the null hypothesis

is accepted. On the other hand, if the p-value is less than ∝, then we can conclude that

the linear model using the set of regressors provides a better fit of the data than the model

with intercept only.

Figure 3.2.5. Best-Subsets Approach (Levine et al., 2008)

19

3.2.6 Assumptions tests

To justify the use of a linear model to make statistical inferences or predict real equity

returns, the linear model obtained must respect the 4 following assumptions: no or low

multicollinearity, independence and normality of residual terms, and homoscedasticity.

Each of these assumptions and the associated test is briefly explained below.

3.2.6.1 Testing for Multicollinearity between Independent Variables

To investigate high correlation between independent variables, a test on multicollinearity

has to be done. A common approach is to use the Variance Inflationary Factor (VIF)

method to test for multicollinearity:

𝑉𝐼𝐹𝑖 =1

1 − 𝑅𝑖2

Where:

VIFi = Variance Inflationary Factor for the independent variable i

𝑅𝑖2 = 𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑟2 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑚𝑜𝑑𝑒𝑙 𝑢𝑠𝑖𝑛𝑔 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝑖 𝑎𝑠 𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡

and other variables (except i) as the independent variables

When a variable has a VIF > 5, it means that the variable has a strong correlation with

another independent variable used in the model and must be eliminated (Levine et al.,

2008).

3.2.6.2 Independence of Residual Terms

To use a linear regression model, it is necessary that the residuals terms be independent.

Given the use of time series data, there must be no autocorrelation between residuals

terms. To verify the independence of the prediction error terms, we use the Durbin-

Watson test. The test statistic of the Durbin-Watson test is given below:

20

𝐷 = ∑ (𝑒𝑖 − 𝑒𝑖−1)2𝑛

𝑖=2

∑ 𝑒𝑖2𝑛

𝑖=1

𝑒𝑖 = 𝑌𝑖 − �̂�𝑖

Where:

𝑛 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑏𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠

𝑒𝑖 = 𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒

𝑌𝑖 = 𝑎𝑐𝑡𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝑌

�̂�𝑖 = 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑣𝑎𝑙𝑢𝑒 𝑓𝑟𝑜𝑚 𝑟𝑒𝑔𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑚𝑜𝑑𝑒𝑙

The value of the test statistic D is then compared to a value in the Durbin-Watson table

corresponding to a significance level ∝, a sample of size n and the number of independent

variables k to conclude if autocorrelation is present or not. That table specifies an upper

value dU as well as a lower value dL. If we have D>dU, then no autocorrelation is present

between residual terms, while on the other hand, if D<dL, then autocorrelation is present

between residual terms. Finally, if dL<D<dU, then it is not possible to conclude whether

autocorrelation is present or not and if further testing is required. When the Durbin-

Watson statistic is inconclusive, Runs test is conducted to conclude whether

autocorrelation of the error terms is present.

3.2.6.3 Normality of Residual Terms

While the distribution of the dependent variables and of the independent variables is not

of significant importance to use a linear regression model, the error terms (𝑒𝑖) must be

normally distributed. There are several options to verify the normality of residuals, one of

them is Kolmogorov-Smirnov test, which is used in this study. Derivation of the residuals’

empirical distribution function is formulated below:

21

𝐹𝑛(𝑥) = 1

𝑛 ∑ 𝐼[−∞,𝑥]

𝑛

𝑖=1

(𝑋𝑖)

Where:

Fn(x) = empirical distribution function

n = number of sample

𝐼[−∞,𝑥] = indicator function, equal to 1 if Xi < x or 0 otherwise

Then, computation of the Kolmogorov Smirnov statistic Dn is as follows:

𝐷𝑛 = 𝑠𝑢𝑝 𝑥

| 𝐹𝑛(𝑥) − 𝐹(𝑥) |

Where 𝐹(𝑥) represents the hypothesizes distribution function. Here, given the

assumption, that hypothetic distribution of residual terms would be a normal distribution.

Then, hypothesis testing is performed, based on the p-value of the above Kolmogorov

Smirnov statistic, where:

• H0: residual value distribution is normal

• Ha: residual value distribution is not normal

If the p-value is greater than the desired level of confidence ∝, then H0 is accepted,

implying that the error terms in the linear model satisfy the normality assumption of linear

regression models. If the p-value is less than ∝, then Ho would be rejected, implying a

violation of the normality assumption.

22

3.2.6.4 Homoscedasticity

Homoscedasticity, or constant variance of the error terms, is also a central assumption of

linear regression models. When heteroscedasticity is present, on the other hand, it

becomes difficult to obtain the error terms for forecast values of the dependent variable,

as the standard deviation in confidence intervals will not be constant across forecast

values. For some values, it will be higher, while it will be lower for some other ones.

Plotting residual terms against predicted values is a graphical way to visualize if

heteroscedasticity is present (See figure 3 below).

Figure 3.2.6.4. Heteroscedasticity and Homoscedasticity Illustration

Heteroscedasticity can be tested using the Glejser test, in which the residuals of the initial

regression are themselves regressed on each variable suspected to have non-constant

variance. More precisely, the absolute value of the residuals is regressed over the

independent variable, as well as two transformations of that same variable (see the 3

regression equations below).

|𝜖𝑖| = 𝛾𝑜 + 𝛾1𝑋1 + 𝛿𝑖

|𝜖𝑖| = 𝛾𝑜 + 𝛾1√𝑋1 + 𝛿𝑖

|𝜖𝑖| = 𝛾𝑜 + 𝛾1

1

𝑋1+ 𝛿𝑖

23

From the 3 regression models above, one with the highest R-squared is selected and is

used to do hypothesis testing, where the null hypothesis is that there is no

heteroscedasticity indication. Based on the p-value, null hypothesis is accepted if the p-

value is greater than ∝, and rejected when p-value is below ∝.

3.2.7 Regression with Heteroscedasticity-Robust Standard Errors

If any of the assumptions regarding independence of errors, normality of error terms, or

homoscedasticity is violated, transformation of one or more of the independent variables

are needed to meet these assumptions. Various method such as using heteroscedastic-

robust standard error and differencing technique (quadratic model and interaction model)

could also be used.

If heteroscedasticity happens to be present, an estimate derived from the linear

regression model is still an unbiased and consistent estimator, meaning that the

estimated coefficient of the regression is not affected. The real issue comes from the fact

that heteroscedasticity might results in the normal standard error to be biased. This

affects the calculations of the t-statistic and F-statistic that often causes Type I error,

which can lead to the rejection of a true null hypothesis H0 (Yamano, 2009).

To correct for heteroscedasticity, several options are available. A popular solution is to

use heteroscedasticity-robust standard errors, also known as the White-Huber standard

errors. These standard errors are typically more conservative than the homoscedastic

standard errors. Even without heteroscedasticity being present, we could use White-

Huber standard errors to calculate t-statistic in ordinary least-square regression (Yamano,

2009).

In the application of using heteroscedasticity-robust standard errors, this study was done

by regressing each model again using the SPSS macro written by Andrew F. Hayes that

accommodates heteroscedastic robust standard errors (Foster, 2011).

24

3.3 Research Hypotheses

Annual real equity returns model:

𝑹𝑬𝑹𝑨 = 𝜷𝟎 + 𝜷𝟏 𝑮𝑰𝑷 + 𝜷𝟐 𝑴𝟐 + 𝜷𝟑𝑼𝟑 + 𝜷𝟒 𝑫𝑪𝑪 + 𝜷𝟓 𝑫𝑯𝑺 + 𝜷𝟔 𝑹𝑷 + 𝜷𝟕 𝑻𝑺

• H0: YoY growth in industrial production, YoY growth in money supply,

unemployment rate, YoY change in consumer confidence, YoY change in housing

starts, risk premium, and term structure do not affect YoY change in real equity

returns.

• Ha: YoY growth in industrial production, YoY growth in money supply,


starts, risk premium, and term structure affect YoY change in real equity returns.

Monthly real equity returns model:

𝑹𝑬𝑹𝑴 = 𝜷𝟎 + 𝜷𝟏 𝑮𝑰𝑷 + 𝜷𝟐 𝑴𝟐 + 𝜷𝟑 𝑼𝟑 + 𝜷𝟒 𝑫𝑪𝑪 + 𝜷𝟓 𝑫𝑯𝑺 + 𝜷𝟔 𝑹𝑷 + 𝜷𝟕 𝑻𝑺

• H0: YoY growth in industrial production, YoY growth in money supply,


starts, risk premium, and term structure do not affect MoM change in real equity

returns.

• Ha: YoY growth in industrial production, YoY growth in money supply,


starts, risk premium, and term structure affect MoM change in real equity returns.

25

Chapter IV

Results and Discussion

4.1 Results

4.1.1 Correlation

Table 6.1 Variables Correlation with Annual Dataset (1972-2013)

To understand the relationship and direction between real equity return and various

economic variables, we conducted Pearson correlation analysis to both annual and

monthly dataset for the period 1972-2013. From the annual data correlation results, we

could observe that real equity return is significantly correlated with YoY growth in

Industrial Production, change in Housing Starts, change in Consumer Confidence, Risk

Premium, next 1-year YoY change in Industrial Production, and next 1-year

unemployment rate.

The result is in-line with the economic intuition that when the economy is on expansion,

equity return is expected to be positive alongside growth in industrial production, housing

starts, and consumer confidence. Meanwhile, good economic condition is also negatively

correlated with risk premium, where the yield spread between risky corporate bonds and

government bonds is expected to be lower.

RER GIP M2 U3 DHS DCC RP TS

LAG_LTR

IR LAG_GIP LAG_M2 LAG_U3

LAG_DH

S

LAG_DC

C LAG_RP LAG_TS

LEAD_LT

RIR

LEAD_GI

P LEAD_M2 LEAD_U3

LEAD_D

HS

LEAD_D

CC

LEAD_R

P LEAD_TS

RER 1 .500** -.056 -.036 .585** .639** -.452** .061 .200 -.105 -.140 .277 .106 .067 .170 .218 .244 .323* -.009 -.359* -.220 -.073 -.210 -.154

GIP 1 -.101 -.319* .383* .673** -.755** .050 .345* .155 .136 .271 .465** .396** -.023 .454** -.103 .122 .000 -.531** -.205 -.349* -.300 -.456**

M2 1 .199 .042 -.038 .302 -.123 -.181 .045 .557** .187 .147 .136 .072 -.170 -.051 .065 .514** .201 -.020 -.051 .028 -.042

U3 1 .203 .063 .473** .435** -.158 -.545** .138 .761** -.283 -.307* .552** .074 .254 .297 .217 .758** .398** .488** .023 .477**

DHS 1 .558** -.212 .214 .158 -.201 -.010 .397** -.119 -.044 .320* .461** .199 .442** .098 -.263 -.119 .221 -.319* -.005

DCC 1 -.484** .362* .064 -.317* .008 .472** .243 -.070 .432** .338* .103 .393* .116 -.309* -.024 -.056 -.337* -.165

RP 1 .193 -.248 -.317* -.021 .033 -.338* -.344* .251 -.168 .153 -.025 .076 .560** .313* .428** .254 .607**

TS 1 .248 -.460** -.057 .479** -.018 -.180 .608** .498** .198 .444** -.212 .078 .472** .342* -.155 .510**

LAG_LTR

IR1 .194 .029 .080 .198 .303 -.116 .442** .441** .105 -.289 -.279 -.061 -.130 -.178 .066

LAG_GIP 1 -.051 -.325* .400** .685** -.748** .067 -.238 -.212 -.077 -.406** -.180 -.229 -.102 -.504**

LAG_M2 1 .170 .095 -.008 .287 -.081 -.135 -.080 .281 .154 -.112 -.131 -.051 -.096

LAG_U3 1 .187 .057 .472** .428** .244 .361* .195 .382* .276 .265 -.209 .218

LAG_DH

S1 .563** -.208 .227 -.057 .060 -.099 -.362* -.071 -.271 -.294 -.218

LAG_DC

C1 -.485** .350* -.136 -.125 .033 -.327* -.116 -.318* -.095 -.372*

LAG_RP 1 .199 .090 .296 -.030 .284 .241 .264 -.053 .461**

LAG_TS 1 -.100 .371* -.334* -.300 .035 .156 -.476** .010

LEAD_LT

RIR1 .196 .027 .084 .196 .285 -.112 .429**

LEAD_GI

P1 -.190 -.294 .393* .659** -.749** .070

LEAD_M21 .263 .037 -.096 .370* -.111

LEAD_U31 .195 .079 .459** .424**

LEAD_D

HS1 .571** -.213 .220

LEAD_D

CC1 -.468

**.382

*

LEAD_R

P1 .185

LEAD_TS1

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

Correlations

26

Interestingly, equity return is positively correlated with the next 1-year YoY growth in

Industrial Production and negatively correlated with next 1-year unemployment rate. This

could potentially mean two things, first is that equity return may have a feedback loop to

the economy, where increasing return translates to confidence on future economic

condition and spurs company to hire more employees. Or second, equity return itself is a

leading indicator of future economic condition, here proxied by industrial production

growth and unemployment rate in the future.

Table 6.2 Variables Correlation with Monthly dataset (1972-2013)

From the monthly dataset correlation analysis, the result confirms the direction of the

relationship between various economic indicator and real equity return. Next one to twelve

months YoY change in Housing Starts is significantly correlated with equity return, which

means that equity return this month may be able to predict Housing Starts figure in the

upcoming 12 months. Similar result is found with Consumer Confidence and Industrial

Production, while the term structure in the past ten to twelve months may be used to

predict the equity return this month.

The results make sense economically because as discussed previously, stock return

could be used as an indicator of future economic condition, which is also proxied by

Housing Starts and Consumer Confidence. Manufacturing activities, as indicated by

GIP M2 DHS DCC RP TS

RER -.043 -.032 .083 .170** -.067 .049

LAG1_GIP LAG1_M2 LAG1_DHS LAG1_DCC LAG1_RP LAG1_TS LEAD1_GIP LEAD1_M2 LEAD1_DHS LEAD1_DCC LEAD1_RP LEAD1_TS

RER -.045 -.038 .063 .064 -.069 .055 RER -.028 -.026 .129** .209** -.069 .041


RER 505 505 505 505 505 505 RER .007 -.021 .173** .177** -.065 .023


RER -.058 -.035 .056 -.005 -.072 .055 RER 0.054 -0.020 .163** .195** -0.064 0.028


RER -.051 -.044 .039 -.001 -.072 .048 RER .082 -.028 .162** .175** -.063 .044


RER -.046 -.048 .013 -.006 -.073 .060 RER .100* -.025 .176** .173** -.064 .040


RER -.041 -.058 .024 -.008 -.072 .078 RER .137** -.023 .204** .195** -.061 .037


RER -.041 -.065 .021 .030 -.071 .080 RER .159** -.024 .200** .196** -.058 .045


RER -.051 -.070 .008 .033 -.071 .080 RER .185** -.032 .183** .206** -.056 .038


RER -.045 -.080 .011 .043 -.066 .087 RER .211** -.031 .183** .211** -.053 .041


RER -.037 -.083 .000 .010 -.063 .093* RER .229** -.027 .171** .209** -.047 .029


RER -.034 -.087* -.030 -.033 -.061 .092* RER .234** -.020 .122** .183** -.048 .030


RER -.024 -.085 -.012 -.006 -.060 .106* RER .256** -.021 .132** .060 -.043 .025

27

growth in Industrial Production, also increase during period of economic expansion, which

is usually priced in by the market five to twelve months before.

4.1.2 Regression and Forecasting Result with Annual Dataset

Following the best-subset approach to build our model for annual time-period, we come

up with two forecasting model that pass various assumption tests in Ordinary Least

Square Regression. These two models are outlined below:

𝑅𝐸𝑅𝐴 = 0.170 + 0.225 𝐷𝐻𝑆 + 0.206 𝐷𝐶𝐶 − 4.55 𝑅𝑃

Adjusted R-Square: 48.1%

𝑅𝐸𝑅𝐴 = 0.064 + 0.213 𝐷𝐻𝑆 + 0.275 𝐷𝐶𝐶


RER: Real Equity Return (% YoY)

DHS: Change in Housing Starts (% YoY)

DCC: Change in Consumer Confidence (% YoY)

RP: Risk Premium or the spread between 10-year BBB US Corporate Bond and

Treasury Bond

Our finding shows that for our model, there is no multicollinearity in the model (VIF <5 for

all variables), no autocorrelation of errors (Durbin-Watson = 1.601 and Runs Test p-value

= 0.639), and the errors are independent (Kolmogorov-Smirnoff p-value = 0.2). However,

there is heteroscedasticity for variable DCC (YoY Change in Consumer Confidence) and

RP (Risk Premium). To avoid rejecting a true H0, we ran the model regression again

using the Heteroscedasticity-Robust Standard Errors (Hayes and Cai, 2007). After

correcting for heteroscedasticity, it was found that the model variables are still significant

at the 10% level. However, we do not necessarily want to be strict in using a precise alpha

(e.g. 5%), as we care more about the prediction ability of the model than mere statistical

28

significance. Our models’ adjusted r-square of 48.1% and 45.8% is slightly higher than

those developed by Peiro (2016) and Fama and Kaul (1981), which are at 44% and 43%

respectively.

Run MATRIX procedure:

HC Method

3

Criterion Variable

RER

Model Fit:

R-sq F df1 df2 p

.5192 16.1017 3.0000 38.0000 .0000

Heteroscedasticity-Consistent Regression Results

Coeff SE(HC) t P>|t|

Constant .1695 .0691 2.4552 .0188

DHS .2250 .0702 3.2039 .0027

DCC .2064 .0795 2.5955 .0134

RP -4.5499 2.5970 -1.7519 .0879

------ END MATRIX -----

From the two different models we developed, we test our models’ prediction ability to

annual, out-of-sample data in the period 2014-2017, the result is shown below; Our model

is based on data from the period 1972-2013. Comparing the Real Equity Return to the

Forecasted Real Equity Return by the two models, it was found that our models have a

decent predicting power for out-of-sample, annual equity return (R-Square 70.01% and

42.59%), but less so when performed on rolling monthly basis (R-Square 5.42% and

14.99%).

Table 4.1.2.1 Forecasting Results using Annual Model

RER (YoY) RER=0.170+0.225 DHS+0.206 DCC-4.55 RP RER=0.064+0.213 DHS+0.275 DCC

2017 17.61% 12.36% 10.86%

2016 8.76% 11.79% 12.07%

2015 0.04% 1.75% 5.18%

2014 13.02% 16.64% 18.02%

R-Square 70.01% 42.59%

29

Table 4.1.2.2 Forecasting Results using Annual Model on Monthly Rolling Time Period

4.1.3 Regression and Forecasting Result with Monthly Dataset

Our investigation of the relationship between various macroeconomic variables on Real

Month-on-Month Stock Return also concludes that change in housing starts and

consumer confidence significantly affect Real Stock Return. Following the exact same

method above on a monthly dataset, we arrived at the equation specified below:

𝑅𝐸𝑅𝑀 = 0.014 − 0.130 𝐿𝐴𝐺11_𝑀2 + 0.028 𝐿𝐸𝐴𝐷6_𝐷𝐻𝑆 + 0.028 𝐿𝐸𝐴𝐷1_𝐷𝐶𝐶


RER: Real Equity Returns (% YoY)

Date

RER

(YoY) Model 1* Model 2** Date

RER

(YoY) Model 1* Model 2**

2017-01-01 15.75% 16.84% 14.25% 2015-07-01 10.40% 13.35% 9.69%

2016-12-01 9.23% 15.36% 12.33% 2015-06-01 6.97% 16.55% 12.11%

2016-11-01 6.07% 13.22% 10.38% 2015-05-01 11.12% 15.22% 12.01%

2016-10-01 2.77% 18.92% 15.81% 2015-04-01 12.32% 16.70% 13.35%

2016-09-01 12.86% 8.05% 4.11% 2015-03-01 11.98% 13.49% 10.37%

2016-08-01 10.74% 11.48% 7.20% 2015-02-01 14.51% 12.90% 10.19%

2016-07-01 4.59% 12.43% 8.03% 2015-01-01 13.51% 19.77% 17.04%

2016-06-01 2.88% 11.68% 7.87% 2014-12-01 12.16% 17.49% 15.22%

2016-05-01 0.65% 11.38% 6.76% 2014-11-01 14.32% 11.84% 9.21%

2016-04-01 0.06% 9.65% 5.10% 2014-10-01 14.32% 19.05% 16.51%

2016-03-01 0.89% 14.11% 9.64% 2014-09-01 16.34% 19.03% 16.80%

2016-02-01 -7.35% 16.64% 11.52% 2014-08-01 20.81% 14.46% 11.21%

2016-01-01 -2.01% 10.18% 5.45% 2014-07-01 13.69% 17.34% 14.24%

2015-12-01 0.71% 10.63% 5.86% 2014-06-01 19.97% 14.34% 11.21%

2015-11-01 2.27% 15.17% 10.91% 2014-05-01 16.47% 12.81% 9.30%

2015-10-01 4.93% 10.23% 6.19% 2014-04-01 16.59% 17.40% 14.04%

2015-09-01 -0.63% 15.96% 11.89% 2014-03-01 18.19% 13.06% 10.48%

2015-08-01 0.24% 16.68% 13.33% 2014-02-01 21.50% 15.96% 14.27%

R-Square Model 1*

R-Square Model 2**

*Model 1: RER=0.170+0.225 DHS+0.206 DCC-4.55 RP

*Model 2: RER=0.064+0.213 DHS+0.275 DCC

5.42%

14.99%

30

LAG11_M2: 11 months lag of Money Supply Growth (% YoY)

LEAD6_DHS: 6 months lead of change in Housing Starts (% YoY)

Lead1_DCC: 1 month lead of change in Consumer Confidence (% YoY)

The model specified above has no multicollinearity (VIF<5) and no positive

autocorrelation among residual terms (Durbin-Watson Test>Du). However, the

assumption on independence of error terms is violated (Kolmogorov-Smirnoff 0.024) and

heteroscedasticity is present on LEAD1_DCC variable (p-value= 0.000). To correct for

this issue, we ran the regression again using heteroscedasticity-robust standard error and

found that all the variables are still significant. Our model’s adjusted r-square is at 6.4%

for monthly real equity return, higher than those developed by Fama and Kaul (1981) that

has r-square of 6% but is lower than Peiro’s (1986) model that has 14% of r-square.


HC Method

3

Criterion Variable

RER

Model Fit:

R-sq F df1 df2 p

.0694 8.6633 3.0000 501.0000 .0000



Constant .0140 .0039 3.5985 .0004

LAG11_M2 -.1296 .0540 -2.4024 .0167

LEAD6_DH .0276 .0095 2.9167 .0037

LEAD1_DC .0278 .0094 2.9672 .0031

------ END MATRIX -----

Using 36 out-of-sample dataset from 2014 to 2016, we use our monthly model to forecast

Month-on-Month Real Equity Return and found that it explains 3.75% of the variation in

Real Equity Return. The forecasting power is not as strong as implementing the model to

31

forecast annual return due to the noise in Real Equity Return on a Month-to-Month basis

that could be impacted by news not directly related to the general economic strength,

such as geopolitical tension, industry scandal, and government policy.

Table 4.1.3 Forecasting Results using Monthly Time Period

4.2 Discussion

Our research shows that equity returns do have relationship with macroeconomic

developments, especially those that commonly considered as leading indicators

themselves such as housing starts and consumer confidence. Contrary to study done by

Flannery and Protopapadakis (2002), we found that equity returns are affected by housing

starts. The difference in results may be attributed to the use of real equity return in our

research instead of nominal equity return and the different time period and interval being

used.

Date

RER

(MoM) Model Date

RER

(MoM) Model

2017-01-01 1.37% 1.32% 2015-07-01 1.92% 1.06%

2016-12-01 1.66% 1.27% 2015-06-01 -2.24% 0.94%

2016-11-01 3.47% 1.07% 2015-05-01 0.95% 1.06%

2016-10-01 -2.11% 1.08% 2015-04-01 0.87% 0.88%

2016-09-01 -0.17% 1.32% 2015-03-01 -1.88% 1.53%

2016-08-01 -0.07% 0.90% 2015-02-01 5.40% 1.42%

2016-07-01 3.63% 0.98% 2015-01-01 -2.44% 1.25%

2016-06-01 -0.01% 1.03% 2014-12-01 0.07% 2.06%

2016-05-01 1.53% 0.81% 2014-11-01 2.82% 1.68%

2016-04-01 0.04% 1.26% 2014-10-01 2.41% 1.55%

2016-03-01 6.36% 0.22% 2014-09-01 -1.42% 1.22%

2016-02-01 0.06% 0.72% 2014-08-01 3.94% 1.17%

2016-01-01 -5.16% 0.65% 2014-07-01 -1.51% 1.42%

2015-12-01 -1.50% 0.48% 2014-06-01 1.91% 1.12%

2015-11-01 0.17% 0.62% 2014-05-01 2.15% 0.54%

2015-10-01 7.97% 0.67% 2014-04-01 0.52% 1.13%

2015-09-01 -2.29% 1.16% 2014-03-01 0.65% 1.21%

2015-08-01 -6.22% 1.64% 2014-02-01 4.40% 1.21%

3.75%

RER=0.014-0.130 LAG11_M2+0.028 LEAD6_DHS+0.028 LEAD1_DCC

R-Squared (3 Year Period)

32

Industrial production has been used by almost all researcher in this topic as an output

variable of the economy. We do find that real equity returns correlates positively with

current and future industrial production change, confirming the results done by Cutler,

Poterba, and Summers (1989) and Canova and De Nicolo (1995). This is not surprising,

as the argument that stock prices reflect investor’s confidence on future economic

condition has long been established by Chen, Roll, and Ross (1986).

A more interesting conclusion is the causality effect between industrial production growth

and real equity returns. Related to this, we found that real equity returns forecast growth

in the future industrial production in the month 5 to 12 and the relationship is significantly

positive. This finding also gives a greater support to Fama (1990) that argues

macroeconomic variables does not predict stock return, but it is stock return that predicts

future macroeconomic development. Fama (1990), Fischer and Merton (1984), and Peiro

(2016) all found that real equity returns forecast future production level one year ahead,

which is in line with our findings, and contrary to the arguments that past industrial

production predicts equity returns (James, Koreisha, and Partch, 1985).

However, the explanatory power of industrial production to real equity returns is low due

to the different implication of economic data surprise to returns in different business cycle.

McQueen and Roley (1993) have similar findings that out of eight macroeconomic

variables being tested, only two becomes significant in its relationship with equity returns,

one of them being month-on-month growth in industrial production. Controlling the

economic regimes increase the number of significant variables from two to six.

Our findings also confirm that changes in money supply does affect real economic

variables, which then affect future stock market returns (Cooper ,1974); Rogalski and

Vinso,1977). Bilson, Brailsford and Hooper (2000) does a similar study in the emerging

market, finding that equity returns were significantly related to lagged money supply,

exchange rate, and weakly related to real activity. However, contrary to few literatures,

we found that money supply is negatively correlated with future real equity returns,

supporting previous findings by Peace and Roley (1985) and Bodie (1976). More

specifically, on our monthly model, lag 11 months of money supply negatively affects real

equity return. Our intuition is that increase in money supply creates inflation in the

33

following periods and force central bank to tighten the monetary policy, which is

detrimental to equity returns. This argument is backed by the findings that changes in real

economic activity affects money supply growth, which then results in expected inflation

and increase in interest rates that is detrimental to equity returns. (Geske and Roll, 1983;

James, Koreisha, and Partch, 1985).

Risk premium does significantly and negatively affect real equity returns, where risk

premium here is defined as the spread between BBB corporate bond yield minus US

Treasury bond yield with 10 years maturity. The result is very intuitive as it is common

that investors shy away from risky assets such as equity and corporate debt altogether

during period of high volatility or poor economic condition (Chen, Roll, and Ross, 1986).

This argument also backs Chen, Roll, and Ross (1986) statement that macroeconomic

variables do serve as a proxy for risk factors in the stock market. On a separate study,

Chen (1989) found that risk premium has a negative relationship with past and future

output growth, which makes it a good proxy of business conditions that indirectly affect

expected equity returns. Although we do not investigate the transmission effect of risk

premium to real equity returns, we do find that risk premium is negatively related to both

growth in industrial production, real equity returns, consumer confidence, and is positively

related to unemployment rate. This result concludes that risk premium tends to be higher

during poor economic condition and it could be attributed to deterioration of market

confidence in the economy.

Ten months to twelve months lags of term spread are also found to have positive effect

on current real equity returns. Although these variables are not significant in our model,

they have a statistically significant correlation with real equity returns. A steepening of the

yield curve is commonly associated with economic expansion, where the long-end of yield

curve increase by more than the short-term end, or the short end of the curve drops by

more than the long end. This could be attributed to higher expectation of inflation in the

long-run as the economy improves, which increases the long-end of the curve, or due to

the policy rate cut that boost the economy through lending activities. Fama and French

(1989) argues that high term spread indicates a business cycle’s bottom and low term

spread indicates peak of the cycle.

34

Relating equity returns with the labor market, the causality between unemployment rates

and equity returns is less clear. It could be that high equity return embeds expectation of

good economic condition ahead, hence lower unemployment rate, or it could be low

unemployment rates is a proxy of good economic condition and moves concurrently with

higher equity returns.

We found that real equity returns predict future 1-year unemployment rate, and the

relationship is negative and significant. The logical explanation is that high equity return

today is an expectation by the market of good economic condition in the future, which is

reflected by among other variables, lower unemployment rate in the next 1-year period.

However, we do not find significant relationship between real equity returns and current

unemployment rates. Boyd, Jagannathan, and Hu (2001) argues that high unemployment

rate has a positive impact to stock price during economic expansion but decreases stock

price during economic contraction. On the other hand, Peiro (2016) states that increase

in employment will also be followed by increase in inflation and interest rates, which is

detrimental to stock price; our findings do not support this hypothesis.

Causality of high stock return and consumer confidence is not clearly determined. Otoo

(1999) argues that high stock returns can lead to increases in consumer confidence

through two channels, first one being that high stock return increases investor wealth,

therefore increasing the consumer confidence. Second, stock market is leading indicator

to the economy; high stock returns are a leading indicator to high income in the future,

therefore boosting consumer confidence. In the paper, Otoo (1999) also found that

consumer confidence is moving concurrently with Wilshire 5000 Index.

Previous literature suggests that high consumer confidence during one period is generally

followed by low equity returns. Fisher and Statman (2002) use consumer confidence

figure that is available by the end of each month to predict returns in the following calendar

month and found that there is a significant relationship between consumer confidence

and subsequent Nasdaq and small cap stock returns, but not to S&P500. They also

documented that high stock returns on various equity indices, including S&P500, are

concurrently moving with an increase in consumer confidence, this relationship is

significant statistically.

35

Our study confirms the later, that as leading indicators, stock returns and consumer

confidence are moving together in the same direction. Change in consumer confidence

and housing starts is a good predictor of equity returns in all three models. Our monthly

RER model incorporates 1-month leading consumer confidence and 6-months leading

change in housing starts as a predictor of current month equity return, which could mean

two things. First, stock market incorporates expectation of future economic condition

faster than consumer confidence and housing starts figure does. Or second, there is a

one month reporting lag for Consumer Confidence figure announcement, from the data

collection process up to the publication in the third week of the month by the Conference

Board. We found no satisfying explanation for the relationship between current real equity

returns and 6-months leading housing starts growth. In fact, it would be interesting to find

out whether high stock return leads real estate developer to construct new private houses,

as economic condition is expected to improve.

𝑅𝐸𝑅𝐴 = 0.170 + 0.225 𝐷𝐻𝑆 + 0.206 𝐷𝐶𝐶 − 4.55 𝑅𝑃

𝑅𝐸𝑅𝐴 = 0.064 + 0.213 𝐷𝐻𝑆 + 0.275 𝐷𝐶𝐶


36

Chapter IV

Conclusion

Our annual model suggests that Real Equity Returns are best explained by the change

in Consumer Confidence, Housing Starts, and Risk Premium during the same period.

Real Equity Return, Consumer Confidence, and Housing Starts are all a proxy for future

economic condition while Risk Premium affects stock return through the risk aversion

prevailing in the market.

As specified by our model, change in Housing Starts and Consumer Confidence have

positive relationship with Real Equity Returns while Risk Premium has a negative

relationship with Real Equity Returns. Meanwhile, higher Risk Premium, often occurring

during economic contraction is associated with lower equity returns, hence the negative

sign.

𝑅𝐸𝑅𝐴 = 0.170 + 0.225 𝐷𝐻𝑆 + 0.206 𝐷𝐶𝐶 − 4.55 𝑅𝑃

𝑅𝐸𝑅𝐴 = 0.064 + 0.213 𝐷𝐻𝑆 + 0.275 𝐷𝐶𝐶

We also found that Real Equity Returns are highly correlated with change in Industrial

Production in the next five to eleven months, although the variable is not significant in our

annual and monthly model. This is consistent with previous literatures that conclude

equity return as a predictor of the next one-year economic output, as often proxied by

industrial production.


37

However, when we use monthly dataset to analyze the relationship in a more discrete

manner, we found that change in consumer confidence moves one month after the

change in real equity returns while change in housing starts moves six months after. We

also note that change in money supply eleven months ago has a negative impact to

current real equity returns, because increase (decrease) in money supply will generally

creates inflation (disinflation/deflation) and forces central bank to tighten (ease) the

monetary policy, which is detrimental (supportive) to equity returns. This also means that

among the group of leading indicators, real equity return is the variable that is most

responsive to expectation regarding future economic condition.

39

Reference

Bates, T. H., Kahle, K. M., Stulz, R. M., 2009. Why Do US Firms Hold So Much

More Cash than They Used To? Journal of Finance, 64, 1985-2021.

Bilson, C. M., Brailsford, T. J., Hooper, V. J., 2000. Selecting Macroeconomic

Variables as Explanatory Factors of Emerging Stock Market Returns. Working

Paper, Australian National University.

Boyd, J. H., Jagannathan, R., Hu, J., 2001. The Stock Market’s Reaction to

Unemployment News: Why Bad News is Usually Good for Stocks. Working

Paper, 8092, NBER.

Canova, F., De Nicoli, G., 1995. Stock Returns and the Business Cycle: A

Structural Approach. European Economics Review, 39, 981-1017.

Chan, K. C., Chen, N. F., Hsieh, D. A., 1985. An Exploratory Investigation of

the Firm Size Effect. Journal of Financial Economics, 14, 451-471.

Chan, L. K. C., Karcesky, J., Lakonishok, J., 1998. The Risk and Return from

Factors. Journal of Financial and Quantitative Analysis, 22, 159-188.

Chen, N. F., Roll, R., Ross, S., 1986. Economic Forces and the Stock Market.

Journal of Business, 59, 383-403.

Cooper, R. V., 1974. Efficient Capital Markets and the Quantity Theory of

Money. Journal of Finance, 24, 887-921.

Cutler, D. M., Poterba, J. M., Summers, L. H., 1989. What Moves Stock Prices?

Journal of Portfolio Management, 15, 4-12.

Day, T. E., 1984. Real Stock Returns and Inflation. Journal of Finance, 39, 493-

502.

40

Fama, E., 1990. Stock Returns, Expected Returns, and Real Activity. Journal

of Finance, 45, 1089-1108.

Fama, E., French, K. R., 1989. Business Conditions and Expected Returns on

Stocks and Bonds. Journal of Financial Economics, 25,23-49

Fama, E. F., Schwert, G. W., 1977. Asset Returns and Inflation. Journal of

Financial Economics, 5, 115-146.

Ferson, W., Harvey, C. R., 1994. Sources of Risk and Expected Returns in

Global Equity Markets. Journal of Banking and Finance, 18, 775-803.

Fischer, S., Merton, R. C., 1984. Macroeconomics and Finance: The Role of

the Stock Market. Carnegie-Rochester Series on Public Policy, 21, 57-108.

Fisher, K. L., Statman, M., 2002. Consumer Confidence and Stock Returns.

Santa Clara University.

Flannery, M. J., Protopapadakis, A. A., 2002. Macroeconomic Factors Do

Influence Aggregate Stock Returns. The Review of Financial Studies, 15, 752-

782.

Geske, R., Roll, R., 1983. The Fiscal and Monetary Linkage Between Stock

Returns and Inflation. Journal of Finance, 38, 1-33.

Hayes A. F., Cai, L. 2007. Using Heteroscedasticity-Consistent Standard

Errors Estimators in OLS Regression: An Introduction and Software

Implementation. Behavior Research Methods, 39, 4, 709-722.

James, C., Koreisha, S., Partch, M., 1985. A VARMA Analysis of the Causal

Relations Among Stock Returns, Real Output, and Nominal Interest Rates.

Keim, D. B., Stambaugh, R. F., 1986. Predicting Returns in the Stock and Bond

Markets. Journal of Financial Economics, 17, 357-390.

41

Lalancette, S., To, M.C, 1997. Performance Attribution Using an APT with

Prespecified Macrofactors and Time-varying Risk Premia and Betas. Journal

of Financial and Quantitative Analysis, 32, 205-224.

Lamont, O., 2001. Economic Tracking Portfolios. Journal of Econometrics, 105,

161-184

Lemmon, M. Portniaguina, E., 2006. Confidence and Asset Prices: Some

Empirical Evidence. The Review of Financial Studies, 19, 4, 1499-1529.

Maroney, N., Protopapadakis, A., 1999. The Book to Market and Size Effect in

a General Asset Pricing Model: Evidence from Seven National Markets.

Working Paper, University of Southern California.

Otoo, M., 1999. Consumer Sentiment and the Stock Market. Federal Reserve

System.

Pearce, D. K., Roley, V. V., 1983. The Reaction of Stock Prices to

Unanticipated Changes in Money: A Note. Journal of Finance, 38, 1323-1333.

Priestley, R., 1996. The Arbitrage Pricing Theory, Macroeconomic and

Financial Factors, and the Expectations Generating Processes. Journal of

Banking and Finance, 20, 869-890.

Rogaliski, R. J., Vinso, J. D., 1977. Stock Returns, Money Supply, and the

Directions of Causality. Journal of Finance, 32, 1017-1030.

Roll, R., 1988. R2. Journal of Finance, 43, 541-566.

Peiro, A., 1996. Stock Prices, Production, and Interest Rates: Comparison of

Three European Countries with the USA. Empirical Economics, 21, 221-234.

Peiro, A., 2016. Stock Prices and Macroeconomic Factors: Some European

Evidence. International Review of Economics and Finance, 41, 287-294.

42

Appendix

6.1 Correlation Table

Table 6.1 Variables Correlation with Annual Dataset (1972-2013)

RER GIP M2 U3 DHS DCC RP TS

LAG_LTR

IR LAG_GIP LAG_M2 LAG_U3

LAG_DH

S

LAG_DC

C LAG_RP LAG_TS

LEAD_LT

RIR

LEAD_GI

P LEAD_M2 LEAD_U3

LEAD_D

HS

LEAD_D

CC

LEAD_R

P LEAD_TS

RER 1 .500** -.056 -.036 .585** .639** -.452** .061 .200 -.105 -.140 .277 .106 .067 .170 .218 .244 .323* -.009 -.359* -.220 -.073 -.210 -.154

GIP 1 -.101 -.319* .383* .673** -.755** .050 .345* .155 .136 .271 .465** .396** -.023 .454** -.103 .122 .000 -.531** -.205 -.349* -.300 -.456**

M2 1 .199 .042 -.038 .302 -.123 -.181 .045 .557** .187 .147 .136 .072 -.170 -.051 .065 .514** .201 -.020 -.051 .028 -.042

U3 1 .203 .063 .473** .435** -.158 -.545** .138 .761** -.283 -.307* .552** .074 .254 .297 .217 .758** .398** .488** .023 .477**

DHS 1 .558** -.212 .214 .158 -.201 -.010 .397** -.119 -.044 .320* .461** .199 .442** .098 -.263 -.119 .221 -.319* -.005

DCC 1 -.484** .362* .064 -.317* .008 .472** .243 -.070 .432** .338* .103 .393* .116 -.309* -.024 -.056 -.337* -.165

RP 1 .193 -.248 -.317* -.021 .033 -.338* -.344* .251 -.168 .153 -.025 .076 .560** .313* .428** .254 .607**

TS 1 .248 -.460** -.057 .479** -.018 -.180 .608** .498** .198 .444** -.212 .078 .472** .342* -.155 .510**

LAG_LTR

IR1 .194 .029 .080 .198 .303 -.116 .442** .441** .105 -.289 -.279 -.061 -.130 -.178 .066

LAG_GIP 1 -.051 -.325* .400** .685** -.748** .067 -.238 -.212 -.077 -.406** -.180 -.229 -.102 -.504**

LAG_M2 1 .170 .095 -.008 .287 -.081 -.135 -.080 .281 .154 -.112 -.131 -.051 -.096

LAG_U3 1 .187 .057 .472** .428** .244 .361* .195 .382* .276 .265 -.209 .218

LAG_DH

S1 .563** -.208 .227 -.057 .060 -.099 -.362* -.071 -.271 -.294 -.218

LAG_DC

C1 -.485** .350* -.136 -.125 .033 -.327* -.116 -.318* -.095 -.372*

LAG_RP 1 .199 .090 .296 -.030 .284 .241 .264 -.053 .461**

LAG_TS 1 -.100 .371* -.334* -.300 .035 .156 -.476** .010

LEAD_LT

RIR1 .196 .027 .084 .196 .285 -.112 .429**

LEAD_GI

P1 -.190 -.294 .393* .659** -.749** .070

LEAD_M21 .263 .037 -.096 .370* -.111

LEAD_U31 .195 .079 .459** .424**

LEAD_D

HS1 .571** -.213 .220

LEAD_D

CC1 -.468

**.382

*

LEAD_R

P1 .185

LEAD_TS1

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

Correlations

43

Table 6.2 Variables Correlation with Monthly Dataset (1972-2013)

GIP M2 DHS DCC RP TS

RER -.043 -.032 .083 .170** -.067 .049


RER -.045 -.038 .063 .064 -.069 .055 RER -.028 -.026 .129** .209** -.069 .041


RER 505 505 505 505 505 505 RER .007 -.021 .173** .177** -.065 .023


RER -.058 -.035 .056 -.005 -.072 .055 RER 0.054 -0.020 .163** .195** -0.064 0.028


RER -.051 -.044 .039 -.001 -.072 .048 RER .082 -.028 .162** .175** -.063 .044


RER -.046 -.048 .013 -.006 -.073 .060 RER .100* -.025 .176** .173** -.064 .040


RER -.041 -.058 .024 -.008 -.072 .078 RER .137** -.023 .204** .195** -.061 .037


RER -.041 -.065 .021 .030 -.071 .080 RER .159** -.024 .200** .196** -.058 .045


RER -.051 -.070 .008 .033 -.071 .080 RER .185** -.032 .183** .206** -.056 .038


RER -.045 -.080 .011 .043 -.066 .087 RER .211** -.031 .183** .211** -.053 .041


RER -.037 -.083 .000 .010 -.063 .093* RER .229** -.027 .171** .209** -.047 .029


RER -.034 -.087* -.030 -.033 -.061 .092* RER .234** -.020 .122** .183** -.048 .030


RER -.024 -.085 -.012 -.006 -.060 .106* RER .256** -.021 .132** .060 -.043 .025

44

6.2 Best Subset Approach with Annual Dataset

Model Summary

Model R R Square Adjusted R

Square Std. Error of the

Estimate

1 .725a .525 .444

.135770996316926

a. Predictors: (Constant), LEAD_U3, DHS, LEAD_GIP, RP, DCC, GIP

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression .713 6 .119 6.450 .000b

Residual .645 35 .018 Total 1.359 41

a. Dependent Variable: RER b. Predictors: (Constant), LEAD_U3, DHS, LEAD_GIP, RP, DCC, GIP

Coefficientsa

Model

Unstandardized Coefficients Standardized Coefficients

t Sig. B Std. Error Beta

1 (Constant) .243 .142 1.707 .097

GIP -.401 .877 -.100 -.457 .651

DHS .219 .095 .340 2.295 .028

DCC .230 .114 .380 2.016 .052

RP -4.931 4.081 -.231 -1.208 .235

LEAD_GIP .035 .598 .008 .058 .954

LEAD_U3 -.876 1.846 -.074 -.475 .638

a. Dependent Variable: RER

Remove Lead_GIP

Model Summary



Estimate

1 .725a .525 .459

.133878404541768

a. Predictors: (Constant), LEAD_U3, DHS, RP, DCC, GIP

45

ANOVAa


1 Regression .713 5 .143 7.959 .000b

Residual .645 36 .018 Total 1.359 41

a. Dependent Variable: RER b. Predictors: (Constant), LEAD_U3, DHS, RP, DCC, GIP

Coefficientsa

Model



1 (Constant) .245 .134 1.832 .075

GIP -.411 .847 -.103 -.485 .630

DHS .220 .091 .342 2.421 .021

DCC .232 .105 .384 2.201 .034

RP -4.891 3.965 -.229 -1.234 .225

LEAD_U3 -.914 1.704 -.077 -.536 .595


Remove LEAD_U3

Model Summary



Estimate

1 .722a .521 .469

.132583368169379

a. Predictors: (Constant), RP, DHS, DCC, GIP

ANOVAa


1 Regression .708 4 .177 10.071 .000b

Residual .650 37 .018 Total 1.359 41

a. Dependent Variable: RER b. Predictors: (Constant), RP, DHS, DCC, GIP

46

Coefficientsa

Model



1 (Constant) .200 .102 1.954 .058

GIP -.325 .824 -.081 -.394 .696

DHS .228 .089 .354 2.568 .014

DCC .224 .103 .371 2.170 .037

RP -5.534 3.742 -.259 -1.479 .148


Remove GIP

Model Summary



Estimate

1 .721a .519 .481

.131101821458765

a. Predictors: (Constant), RP, DHS, DCC

ANOVAa


1 Regression .705 3 .235 13.680 .000b

Residual .653 38 .017 Total 1.359 41

a. Dependent Variable: RER b. Predictors: (Constant), RP, DHS, DCC

Coefficientsa

Model



1 (Constant) .170 .067 2.518 .016

DHS .225 .087 .350 2.572 .014

DCC .206 .092 .342 2.249 .030

RP -4.550 2.757 -.213 -1.650 .107


47

Remove RP

Model Summary



Estimate

1 .696a .485 .458

.133967368442757

a. Predictors: (Constant), DCC, DHS

ANOVAa


1 Regression .659 2 .329 18.348 .000b

Residual .700 39 .018 Total 1.359 41

a. Dependent Variable: RER b. Predictors: (Constant), DCC, DHS

Coefficientsa

Model



1 (Constant) .064 .021 3.061 .004

DHS .213 .089 .332 2.396 .021

DCC .275 .084 .455 3.283 .002


6.3 Assumption Tests with Annual Dataset

6.3.1 Durbin-Watson Test and Runs Test

Model Summaryb



Estimate Durbin-Watson

1 .721a .519 .481

.131101821458765

1.601

a. Predictors: (Constant), RP, DHS, DCC b. Dependent Variable: RER

48

Runs Test

Unstandardized

Residual

Test Valuea .01024 Cases < Test Value 21 Cases >= Test Value 21 Total Cases 42 Number of Runs 20 Z -.469 Asymp. Sig. (2-tailed) .639

a. Median

6.3.2 Kolmogorov-Smirnoff Test

One-Sample Kolmogorov-Smirnov Test

Unstandardized

Residual

N 42 Normal Parametersa,b Mean .0000000

Std. Deviation .12621431 Most Extreme Differences Absolute .096

Positive .062 Negative -.096

Test Statistic .096 Asymp. Sig. (2-tailed) .200c,d

a. Test distribution is Normal. b. Calculated from data. c. Lilliefors Significance Correction. d. This is a lower bound of the true significance.

6.3.3 Glejser Test Coefficientsa

Model



1 (Constant) .176 .034 5.124 .000

DHS .065 .045 .258 1.465 .151

DCC -.132 .047 -.553 -2.810 .008

RP -3.103 1.408 -.369 -2.204 .034

a. Dependent Variable: RES2

49

6.3.4 VIF Test Coefficientsa

Model


t Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) .170 .067 2.518 .016 DHS .225 .087 .350 2.572 .014 .685 1.460

DCC .206 .092 .342 2.249 .030 .549 1.822

RP -4.550 2.757 -.213 -1.650 .107 .761 1.315


Collinearity Diagnosticsa

Model Dimension Eigenvalue Condition Index

Variance Proportions

(Constant) DHS DCC RP

1 1 2.032 1.000 .02 .03 .02 .02

2 1.474 1.174 .01 .18 .16 .00

3 .449 2.127 .00 .77 .58 .00

4 .045 6.737 .97 .01 .24 .98


6.4 Regression with White Standard Error (Annual Dataset)


HC Method

3

Criterion Variable

RER

Model Fit:

R-sq F df1 df2 p

.5192 16.1017 3.0000 38.0000 .0000



Constant .1695 .0691 2.4552 .0188

DHS .2250 .0702 3.2039 .0027

DCC .2064 .0795 2.5955 .0134

50

RP -4.5499 2.5970 -1.7519 .0879

------ END MATRIX -----

6.5 Best Subset Approach with Monthly Dataset

Model Summary



Estimate

1 .418a .174 .109

.042648051885917

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD7_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD3_DCC, LEAD10_DCC, LEAD4_DHS, LEAD5_DCC, LEAD11_DHS, LEAD7_DHS, LEAD2_DHS, LEAD10_DHS, LEAD5_DHS, LEAD12_GIP, LEAD6_DHS, LEAD3_DHS, LEAD9_DHS, LEAD2_DCC, LEAD8_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LEAD6_DCC, LEAD4_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD9_GIP, LEAD11_GIP, LEAD6_GIP, LEAD8_GIP, LAG11_TS

ANOVAa


1 Regression .180 37 .005 2.667 .000b

Residual .849 467 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD7_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD3_DCC, LEAD10_DCC, LEAD4_DHS, LEAD5_DCC, LEAD11_DHS, LEAD7_DHS, LEAD2_DHS, LEAD10_DHS, LEAD5_DHS, LEAD12_GIP, LEAD6_DHS, LEAD3_DHS, LEAD9_DHS, LEAD2_DCC, LEAD8_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LEAD6_DCC, LEAD4_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD9_GIP, LEAD11_GIP, LEAD6_GIP, LEAD8_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.667 .096

51

DCC -.007 .019 -.037 -.341 .733

LEAD5_GIP -.275 .240 -.289 -1.145 .253

LEAD6_GIP .180 .339 .189 .532 .595

LEAD7_GIP -.115 .339 -.121 -.340 .734

LEAD8_GIP .142 .342 .148 .416 .678

LEAD9_GIP .064 .343 .067 .187 .852

LEAD10_GIP .150 .342 .155 .438 .662

LEAD11_GIP -.690 .340 -.714 -2.028 .043

LEAD12_GIP .614 .245 .633 2.505 .013

LAG11_M2 -.105 .066 -.068 -1.588 .113

LAG10_TS .363 .981 .090 .370 .711

LAG11_TS -1.379 1.451 -.341 -.951 .342

LAG12_TS 1.050 .992 .259 1.059 .290

LEAD1_DHS -.030 .019 -.155 -1.589 .113

LEAD2_DHS .031 .021 .159 1.467 .143

LEAD3_DHS .011 .022 .059 .524 .600

LEAD4_DHS -.016 .022 -.081 -.729 .466

LEAD5_DHS -.006 .022 -.033 -.292 .771

LEAD6_DHS .026 .022 .135 1.182 .238

LEAD7_DHS .002 .022 .010 .089 .929

LEAD8_DHS -.013 .022 -.069 -.599 .549

LEAD9_DHS .016 .022 .085 .745 .457

LEAD10_DHS .005 .022 .026 .232 .817

LEAD11_DHS -.035 .021 -.183 -1.697 .090

LEAD12_DHS .021 .019 .109 1.106 .269

LEAD1_DCC .056 .025 .317 2.250 .025

LEAD2_DCC -.045 .025 -.255 -1.803 .072

LEAD3_DCC .025 .025 .142 .999 .318

LEAD4_DCC -.001 .025 -.003 -.021 .984

LEAD5_DCC -.011 .025 -.061 -.427 .669

LEAD6_DCC .001 .025 .006 .045 .965

LEAD7_DCC -.001 .025 -.006 -.045 .965

LEAD8_DCC -.002 .025 -.009 -.063 .950

LEAD9_DCC -.008 .025 -.046 -.324 .746

LEAD10_DCC .009 .025 .053 .369 .712

LEAD11_DCC .085 .025 .479 3.407 .001

LEAD12_DCC -.094 .019 -.528 -4.983 .000

52


Remove LEAD4_DCC Model Summary



Estimate

1 .418a .174 .111

.042602482722191

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD7_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD3_DCC, LEAD10_DCC, LEAD4_DHS, LEAD5_DCC, LEAD11_DHS, LEAD7_DHS, LEAD2_DHS, LEAD10_DHS, LEAD5_DHS, LEAD12_GIP, LEAD6_DHS, LEAD3_DHS, LEAD9_DHS, LEAD2_DCC, LEAD8_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LEAD6_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD9_GIP, LEAD6_GIP, LAG11_TS

ANOVAa


1 Regression .180 36 .005 2.747 .000b

Residual .849 468 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD7_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD3_DCC, LEAD10_DCC, LEAD4_DHS, LEAD5_DCC, LEAD11_DHS, LEAD7_DHS, LEAD2_DHS, LEAD10_DHS, LEAD5_DHS, LEAD12_GIP, LEAD6_DHS, LEAD3_DHS, LEAD9_DHS, LEAD2_DCC, LEAD8_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LEAD6_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD9_GIP, LEAD6_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.668 .096

DCC -.007 .019 -.037 -.342 .733

LEAD5_GIP -.274 .238 -.288 -1.153 .250

LEAD6_GIP .180 .338 .189 .533 .595

53

LEAD7_GIP -.116 .336 -.122 -.345 .730

LEAD8_GIP .143 .338 .149 .423 .673

LEAD9_GIP .064 .341 .066 .186 .852

LEAD10_GIP .150 .341 .155 .438 .661

LEAD11_GIP -.690 .339 -.715 -2.033 .043

LEAD12_GIP .614 .244 .634 2.520 .012

LAG11_M2 -.105 .066 -.068 -1.590 .112

LAG10_TS .364 .978 .090 .372 .710

LAG11_TS -1.381 1.447 -.341 -.955 .340

LAG12_TS 1.051 .991 .260 1.061 .289

LEAD1_DHS -.030 .019 -.155 -1.592 .112

LEAD2_DHS .031 .021 .159 1.470 .142

LEAD3_DHS .011 .022 .059 .524 .600

LEAD4_DHS -.016 .022 -.081 -.730 .466

LEAD5_DHS -.006 .022 -.033 -.292 .770

LEAD6_DHS .026 .022 .135 1.185 .237

LEAD7_DHS .002 .022 .010 .089 .929

LEAD8_DHS -.013 .022 -.069 -.600 .549

LEAD9_DHS .016 .022 .085 .750 .454

LEAD10_DHS .005 .022 .026 .232 .817

LEAD11_DHS -.035 .021 -.183 -1.701 .090

LEAD12_DHS .021 .019 .109 1.107 .269

LEAD1_DCC .056 .025 .317 2.259 .024

LEAD2_DCC -.045 .025 -.255 -1.810 .071

LEAD3_DCC .025 .022 .141 1.130 .259

LEAD5_DCC -.011 .022 -.062 -.499 .618

LEAD6_DCC .001 .025 .006 .043 .965

LEAD7_DCC -.001 .025 -.006 -.043 .966

LEAD8_DCC -.002 .025 -.009 -.062 .950

LEAD9_DCC -.008 .025 -.047 -.328 .743

LEAD10_DCC .009 .025 .053 .373 .709

LEAD11_DCC .085 .025 .479 3.411 .001

LEAD12_DCC -.094 .019 -.528 -4.994 .000


Remove LEAD7_DCC

54

Model Summary



Estimate

1 .418a .174 .113

.042557123479518

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD8_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD5_DCC, LEAD2_DCC, LEAD4_DHS, LEAD10_DCC, LEAD11_DHS, LEAD7_DHS, LEAD2_DHS, LEAD10_DHS, LEAD5_DHS, LEAD12_GIP, LEAD6_DHS, LEAD3_DHS, LEAD9_DHS, LEAD6_DCC, LEAD3_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LAG11_TS, LEAD6_GIP, LEAD9_GIP

ANOVAa


1 Regression .179 35 .005 2.832 .000b

Residual .849 469 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD8_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD5_DCC, LEAD2_DCC, LEAD4_DHS, LEAD10_DCC, LEAD11_DHS, LEAD7_DHS, LEAD2_DHS, LEAD10_DHS, LEAD5_DHS, LEAD12_GIP, LEAD6_DHS, LEAD3_DHS, LEAD9_DHS, LEAD6_DCC, LEAD3_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LAG11_TS, LEAD6_GIP, LEAD9_GIP

Coefficientsa

Model



1 (Constant) .010 .006 1.672 .095

DCC -.007 .019 -.038 -.347 .729

LEAD5_GIP -.274 .237 -.288 -1.153 .249

LEAD6_GIP .180 .338 .189 .534 .594

LEAD7_GIP -.117 .335 -.123 -.349 .727

LEAD8_GIP .144 .336 .151 .429 .668

LEAD9_GIP .063 .341 .066 .186 .853

55

LEAD10_GIP .147 .337 .153 .437 .662

LEAD11_GIP -.688 .336 -.713 -2.050 .041

LEAD12_GIP .613 .243 .633 2.526 .012

LAG11_M2 -.105 .066 -.068 -1.592 .112

LAG10_TS .361 .975 .089 .371 .711

LAG11_TS -1.375 1.438 -.340 -.956 .339

LAG12_TS 1.048 .986 .259 1.062 .289

LEAD1_DHS -.030 .019 -.155 -1.598 .111

LEAD2_DHS .031 .021 .159 1.476 .141

LEAD3_DHS .011 .022 .058 .525 .600

LEAD4_DHS -.016 .022 -.081 -.731 .465

LEAD5_DHS -.006 .022 -.033 -.291 .771

LEAD6_DHS .026 .022 .135 1.185 .237

LEAD7_DHS .002 .022 .010 .090 .929

LEAD8_DHS -.013 .022 -.069 -.602 .547

LEAD9_DHS .016 .022 .085 .752 .453

LEAD10_DHS .005 .022 .026 .232 .817

LEAD11_DHS -.035 .021 -.183 -1.705 .089

LEAD12_DHS .021 .019 .109 1.113 .266

LEAD1_DCC .056 .025 .317 2.278 .023

LEAD2_DCC -.045 .025 -.256 -1.826 .068

LEAD3_DCC .025 .022 .141 1.140 .255

LEAD5_DCC -.011 .022 -.062 -.500 .617

LEAD6_DCC .001 .022 .003 .026 .980

LEAD8_DCC -.002 .022 -.012 -.095 .924

LEAD9_DCC -.008 .025 -.047 -.333 .739

LEAD10_DCC .010 .025 .054 .380 .704

LEAD11_DCC .085 .025 .479 3.418 .001

LEAD12_DCC -.094 .019 -.528 -5.018 .000


Remove LEAD6_DCC

Model Summary



Estimate

1 .418a .174 .115

.042511855493265

56

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD8_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD5_DCC, LEAD2_DCC, LEAD4_DHS, LEAD10_DCC, LEAD11_DHS, LEAD7_DHS, LEAD2_DHS, LEAD10_DHS, LEAD5_DHS, LEAD12_GIP, LEAD6_DHS, LEAD3_DHS, LEAD9_DHS, LEAD3_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LAG12_TS, LEAD5_GIP, LEAD9_GIP, LEAD11_GIP, LEAD10_GIP, LEAD8_GIP, LEAD6_GIP, LAG11_TS

ANOVAa


1 Regression .179 34 .005 2.921 .000b

Residual .849 470 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD8_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD5_DCC, LEAD2_DCC, LEAD4_DHS, LEAD10_DCC, LEAD11_DHS, LEAD7_DHS, LEAD2_DHS, LEAD10_DHS, LEAD5_DHS, LEAD12_GIP, LEAD6_DHS, LEAD3_DHS, LEAD9_DHS, LEAD3_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LAG12_TS, LEAD5_GIP, LEAD9_GIP, LEAD11_GIP, LEAD10_GIP, LEAD8_GIP, LEAD6_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.673 .095

DCC -.007 .019 -.038 -.349 .728

LEAD5_GIP -.274 .237 -.288 -1.159 .247

LEAD6_GIP .181 .336 .190 .538 .591

LEAD7_GIP -.118 .333 -.123 -.354 .724

LEAD8_GIP .144 .336 .151 .429 .668

LEAD9_GIP .065 .335 .068 .194 .846

LEAD10_GIP .147 .336 .153 .437 .662

LEAD11_GIP -.688 .335 -.713 -2.052 .041

LEAD12_GIP .613 .243 .633 2.528 .012

LAG11_M2 -.105 .066 -.068 -1.594 .112

LAG10_TS .362 .974 .089 .371 .711

LAG11_TS -1.375 1.437 -.340 -.957 .339

57

LAG12_TS 1.047 .985 .259 1.063 .288

LEAD1_DHS -.030 .019 -.156 -1.601 .110

LEAD2_DHS .031 .021 .159 1.478 .140

LEAD3_DHS .011 .022 .059 .527 .599

LEAD4_DHS -.016 .022 -.082 -.733 .464

LEAD5_DHS -.006 .022 -.033 -.291 .771

LEAD6_DHS .026 .022 .135 1.187 .236

LEAD7_DHS .002 .022 .010 .090 .928

LEAD8_DHS -.013 .022 -.069 -.604 .546

LEAD9_DHS .016 .022 .085 .752 .452

LEAD10_DHS .005 .022 .026 .233 .816

LEAD11_DHS -.035 .021 -.183 -1.713 .087

LEAD12_DHS .021 .019 .109 1.118 .264

LEAD1_DCC .056 .025 .318 2.285 .023

LEAD2_DCC -.045 .025 -.256 -1.828 .068

LEAD3_DCC .025 .022 .141 1.147 .252

LEAD5_DCC -.011 .016 -.060 -.666 .505

LEAD8_DCC -.002 .020 -.011 -.093 .926

LEAD9_DCC -.008 .025 -.047 -.336 .737

LEAD10_DCC .009 .025 .053 .379 .705

LEAD11_DCC .085 .025 .479 3.439 .001

LEAD12_DCC -.094 .019 -.528 -5.030 .000


Remove LEAD7_DHS

Model Summary



Estimate

1 .418a .174 .117

.042467070102140

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD8_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD5_DCC, LEAD2_DCC, LEAD4_DHS, LEAD10_DCC, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD5_DHS, LEAD10_DHS, LEAD3_DHS, LEAD3_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LAG12_TS, LEAD5_GIP, LEAD9_GIP, LEAD11_GIP, LEAD10_GIP, LEAD6_GIP, LEAD8_GIP, LAG11_TS

58

ANOVAa


1 Regression .179 33 .005 3.016 .000b

Residual .849 471 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD8_DCC, LEAD1_DHS, DCC, LEAD12_DHS, LEAD5_DCC, LEAD2_DCC, LEAD4_DHS, LEAD10_DCC, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD5_DHS, LEAD10_DHS, LEAD3_DHS, LEAD3_DCC, LEAD11_DCC, LEAD1_DCC, LEAD9_DCC, LAG12_TS, LEAD5_GIP, LEAD9_GIP, LEAD11_GIP, LEAD10_GIP, LEAD6_GIP, LEAD8_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.675 .095

DCC -.007 .019 -.038 -.348 .728

LEAD5_GIP -.275 .236 -.289 -1.164 .245

LEAD6_GIP .177 .334 .186 .531 .596

LEAD7_GIP -.115 .332 -.121 -.348 .728

LEAD8_GIP .144 .336 .151 .429 .668

LEAD9_GIP .070 .330 .073 .211 .833

LEAD10_GIP .145 .335 .151 .433 .665

LEAD11_GIP -.688 .335 -.713 -2.055 .040

LEAD12_GIP .612 .242 .632 2.530 .012

LAG11_M2 -.105 .066 -.068 -1.596 .111

LAG10_TS .362 .973 .090 .372 .710

LAG11_TS -1.380 1.434 -.341 -.962 .337

LAG12_TS 1.051 .983 .260 1.069 .286

LEAD1_DHS -.030 .019 -.156 -1.604 .109

LEAD2_DHS .031 .021 .159 1.482 .139

LEAD3_DHS .011 .021 .059 .537 .592

LEAD4_DHS -.016 .021 -.081 -.729 .466

LEAD5_DHS -.006 .021 -.031 -.281 .779

LEAD6_DHS .026 .022 .137 1.226 .221

59

LEAD8_DHS -.013 .022 -.067 -.598 .550

LEAD9_DHS .017 .022 .087 .779 .437

LEAD10_DHS .005 .021 .027 .241 .810

LEAD11_DHS -.035 .021 -.183 -1.713 .087

LEAD12_DHS .021 .019 .109 1.122 .262

LEAD1_DCC .056 .025 .318 2.299 .022

LEAD2_DCC -.045 .025 -.256 -1.842 .066

LEAD3_DCC .025 .022 .141 1.147 .252

LEAD5_DCC -.011 .016 -.061 -.671 .503

LEAD8_DCC -.002 .020 -.010 -.090 .928

LEAD9_DCC -.008 .025 -.048 -.338 .735

LEAD10_DCC .010 .025 .054 .383 .702

LEAD11_DCC .085 .025 .480 3.446 .001

LEAD12_DCC -.094 .019 -.529 -5.048 .000


Remove LEAD8_DCC

Model Summary



Estimate

1 .418a .174 .118

.042422425302884

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, DCC, LEAD9_DCC, LEAD5_DCC, LEAD12_DHS, LEAD2_DCC, LEAD4_DHS, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD5_DHS, LEAD10_DHS, LEAD3_DHS, LEAD11_DCC, LEAD3_DCC, LEAD10_DCC, LEAD1_DCC, LAG12_TS, LEAD5_GIP, LEAD9_GIP, LEAD11_GIP, LEAD10_GIP, LEAD6_GIP, LEAD8_GIP, LAG11_TS

ANOVAa


1 Regression .179 32 .006 3.116 .000b

Residual .849 472 .002 Total 1.029 504


60

b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, DCC, LEAD9_DCC, LEAD5_DCC, LEAD12_DHS, LEAD2_DCC, LEAD4_DHS, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD5_DHS, LEAD10_DHS, LEAD3_DHS, LEAD11_DCC, LEAD3_DCC, LEAD10_DCC, LEAD1_DCC, LAG12_TS, LEAD5_GIP, LEAD9_GIP, LEAD11_GIP, LEAD10_GIP, LEAD6_GIP, LEAD8_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.683 .093

DCC -.007 .019 -.039 -.356 .722

LEAD5_GIP -.273 .235 -.288 -1.162 .246

LEAD6_GIP .178 .333 .186 .533 .594

LEAD7_GIP -.115 .331 -.120 -.347 .729

LEAD8_GIP .143 .335 .150 .428 .669

LEAD9_GIP .071 .330 .074 .215 .830

LEAD10_GIP .142 .333 .148 .427 .669

LEAD11_GIP -.691 .333 -.716 -2.074 .039

LEAD12_GIP .615 .240 .634 2.559 .011

LAG11_M2 -.105 .066 -.069 -1.599 .110

LAG10_TS .365 .971 .090 .376 .707

LAG11_TS -1.379 1.433 -.341 -.962 .336

LAG12_TS 1.047 .981 .259 1.067 .286

LEAD1_DHS -.030 .019 -.155 -1.604 .109

LEAD2_DHS .031 .021 .159 1.482 .139

LEAD3_DHS .012 .021 .060 .540 .590

LEAD4_DHS -.016 .021 -.081 -.734 .464

LEAD5_DHS -.006 .021 -.031 -.283 .777

LEAD6_DHS .027 .022 .137 1.229 .220

LEAD8_DHS -.013 .022 -.067 -.598 .550

LEAD9_DHS .017 .022 .087 .778 .437

LEAD10_DHS .005 .021 .027 .245 .806

LEAD11_DHS -.035 .021 -.182 -1.714 .087

LEAD12_DHS .021 .019 .109 1.122 .263

LEAD1_DCC .056 .024 .318 2.300 .022

LEAD2_DCC -.045 .025 -.256 -1.843 .066

61

LEAD3_DCC .025 .022 .141 1.147 .252

LEAD5_DCC -.011 .016 -.062 -.704 .482

LEAD9_DCC -.010 .020 -.055 -.494 .622

LEAD10_DCC .010 .025 .054 .383 .702

LEAD11_DCC .086 .025 .481 3.471 .001

LEAD12_DCC -.094 .019 -.529 -5.064 .000


Remove LEAD9_GIP

Model Summary



Estimate

1 .418a .174 .120

.042379628991248

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, DCC, LEAD9_DCC, LEAD5_DCC, LEAD12_DHS, LEAD2_DCC, LEAD4_DHS, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD5_DHS, LEAD10_DHS, LEAD3_DHS, LEAD11_DCC, LEAD3_DCC, LEAD10_DCC, LEAD1_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD6_GIP, LAG11_TS

ANOVAa


1 Regression .179 31 .006 3.222 .000b

Residual .850 473 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, DCC, LEAD9_DCC, LEAD5_DCC, LEAD12_DHS, LEAD2_DCC, LEAD4_DHS, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD5_DHS, LEAD10_DHS, LEAD3_DHS, LEAD11_DCC, LEAD3_DCC, LEAD10_DCC, LEAD1_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD6_GIP, LAG11_TS

Coefficientsa

Model



62

1 (Constant) .010 .006 1.681 .093

DCC -.007 .019 -.038 -.351 .726

LEAD5_GIP -.274 .235 -.289 -1.167 .244

LEAD6_GIP .179 .333 .188 .537 .591

LEAD7_GIP -.115 .331 -.121 -.348 .728

LEAD8_GIP .179 .291 .187 .616 .538

LEAD10_GIP .178 .290 .185 .613 .540

LEAD11_GIP -.693 .333 -.717 -2.081 .038

LEAD12_GIP .617 .240 .636 2.571 .010

LAG11_M2 -.105 .066 -.069 -1.601 .110

LAG10_TS .379 .968 .094 .392 .695

LAG11_TS -1.412 1.423 -.349 -.992 .322

LAG12_TS 1.066 .976 .263 1.092 .275

LEAD1_DHS -.030 .019 -.156 -1.618 .106

LEAD2_DHS .031 .021 .160 1.490 .137

LEAD3_DHS .012 .021 .060 .541 .589

LEAD4_DHS -.016 .021 -.081 -.735 .462

LEAD5_DHS -.006 .021 -.030 -.276 .782

LEAD6_DHS .026 .022 .137 1.225 .221

LEAD8_DHS -.013 .022 -.066 -.595 .552

LEAD9_DHS .017 .021 .089 .808 .420

LEAD10_DHS .005 .021 .024 .223 .824

LEAD11_DHS -.035 .021 -.184 -1.727 .085

LEAD12_DHS .021 .019 .110 1.133 .258

LEAD1_DCC .056 .024 .315 2.292 .022

LEAD2_DCC -.045 .024 -.254 -1.836 .067

LEAD3_DCC .025 .022 .141 1.150 .251

LEAD5_DCC -.011 .016 -.062 -.703 .483

LEAD9_DCC -.010 .020 -.054 -.481 .631

LEAD10_DCC .009 .025 .053 .375 .708

LEAD11_DCC .086 .025 .481 3.475 .001

LEAD12_DCC -.094 .019 -.530 -5.087 .000


Remove LEAD10_DHS

Model Summary

63



Estimate

1 .417a .174 .122

.042337118295141

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, DCC, LEAD9_DCC, LEAD5_DCC, LEAD12_DHS, LEAD2_DCC, LEAD4_DHS, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD5_DHS, LEAD3_DHS, LEAD11_DCC, LEAD3_DCC, LEAD10_DCC, LEAD1_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD6_GIP, LAG11_TS

ANOVAa


1 Regression .179 30 .006 3.334 .000b

Residual .850 474 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, DCC, LEAD9_DCC, LEAD5_DCC, LEAD12_DHS, LEAD2_DCC, LEAD4_DHS, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD5_DHS, LEAD3_DHS, LEAD11_DCC, LEAD3_DCC, LEAD10_DCC, LEAD1_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD6_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.676 .094

DCC -.007 .019 -.038 -.357 .722

LEAD5_GIP -.274 .235 -.288 -1.167 .244

LEAD6_GIP .178 .333 .187 .537 .592

LEAD7_GIP -.111 .330 -.117 -.337 .736

LEAD8_GIP .171 .288 .178 .593 .554

LEAD10_GIP .179 .289 .186 .620 .536

LEAD11_GIP -.693 .332 -.718 -2.086 .038

LEAD12_GIP .621 .239 .641 2.600 .010

LAG11_M2 -.105 .065 -.068 -1.600 .110

LAG10_TS .393 .965 .097 .407 .684

64

LAG11_TS -1.440 1.416 -.356 -1.017 .310

LAG12_TS 1.082 .972 .267 1.113 .266

LEAD1_DHS -.030 .019 -.156 -1.619 .106

LEAD2_DHS .031 .021 .159 1.486 .138

LEAD3_DHS .011 .021 .059 .532 .595

LEAD4_DHS -.016 .021 -.081 -.732 .464

LEAD5_DHS -.006 .021 -.029 -.268 .789

LEAD6_DHS .027 .021 .139 1.251 .211

LEAD8_DHS -.012 .021 -.062 -.563 .573

LEAD9_DHS .018 .021 .095 .876 .382

LEAD11_DHS -.034 .020 -.178 -1.728 .085

LEAD12_DHS .022 .018 .115 1.226 .221

LEAD1_DCC .056 .024 .316 2.301 .022

LEAD2_DCC -.045 .024 -.255 -1.842 .066

LEAD3_DCC .025 .022 .142 1.166 .244

LEAD5_DCC -.011 .016 -.063 -.721 .471

LEAD9_DCC -.009 .020 -.053 -.476 .634

LEAD10_DCC .009 .025 .052 .368 .713

LEAD11_DCC .086 .025 .481 3.481 .001

LEAD12_DCC -.094 .019 -.531 -5.093 .000


Remove LEAD5_DHS

Model Summary



Estimate

1 .417a .174 .124

.042295730643247

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, DCC, LEAD9_DCC, LEAD5_DCC, LEAD12_DHS, LEAD2_DCC, LEAD4_DHS, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD3_DHS, LEAD11_DCC, LEAD3_DCC, LEAD10_DCC, LEAD1_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD6_GIP, LAG11_TS

ANOVAa

65


1 Regression .179 29 .006 3.454 .000b

Residual .850 475 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, DCC, LEAD9_DCC, LEAD5_DCC, LEAD12_DHS, LEAD2_DCC, LEAD4_DHS, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD3_DHS, LEAD11_DCC, LEAD3_DCC, LEAD10_DCC, LEAD1_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD6_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.677 .094

DCC -.007 .019 -.037 -.347 .729

LEAD5_GIP -.269 .234 -.283 -1.152 .250

LEAD6_GIP .181 .332 .190 .545 .586

LEAD7_GIP -.125 .326 -.131 -.385 .701

LEAD8_GIP .174 .287 .182 .605 .545

LEAD10_GIP .181 .289 .188 .625 .532

LEAD11_GIP -.692 .332 -.716 -2.083 .038

LEAD12_GIP .621 .239 .640 2.602 .010

LAG11_M2 -.105 .065 -.068 -1.600 .110

LAG10_TS .398 .964 .098 .413 .680

LAG11_TS -1.448 1.414 -.358 -1.024 .306

LAG12_TS 1.086 .971 .268 1.118 .264

LEAD1_DHS -.031 .019 -.158 -1.648 .100

LEAD2_DHS .031 .021 .158 1.477 .140

LEAD3_DHS .010 .021 .053 .491 .624

LEAD4_DHS -.017 .021 -.088 -.817 .414

LEAD6_DHS .025 .021 .132 1.224 .222

LEAD8_DHS -.013 .021 -.065 -.597 .551

LEAD9_DHS .018 .021 .091 .850 .396

LEAD11_DHS -.035 .020 -.179 -1.740 .083

LEAD12_DHS .022 .018 .116 1.235 .218

66

LEAD1_DCC .056 .024 .317 2.307 .021

LEAD2_DCC -.045 .024 -.254 -1.839 .067

LEAD3_DCC .025 .022 .142 1.166 .244

LEAD5_DCC -.011 .016 -.064 -.734 .463

LEAD9_DCC -.010 .020 -.055 -.493 .622

LEAD10_DCC .009 .025 .053 .380 .704

LEAD11_DCC .085 .025 .480 3.479 .001

LEAD12_DCC -.094 .019 -.529 -5.092 .000


Remove DCC

Model Summary



Estimate

1 .417a .174 .125

.042256629698358

a. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, LEAD9_DCC, LEAD1_DCC, LEAD5_DCC, LEAD12_DHS, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD3_DHS, LEAD11_DCC, LEAD2_DCC, LEAD10_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD6_GIP, LAG11_TS

ANOVAa


1 Regression .179 28 .006 3.579 .000b

Residual .850 476 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD7_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD1_DHS, LEAD9_DCC, LEAD1_DCC, LEAD5_DCC, LEAD12_DHS, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD12_GIP, LEAD3_DHS, LEAD11_DCC, LEAD2_DCC, LEAD10_DCC, LAG12_TS, LEAD5_GIP, LEAD10_GIP, LEAD8_GIP, LEAD11_GIP, LEAD6_GIP, LAG11_TS

Coefficientsa

67

Model



1 (Constant) .010 .006 1.695 .091

LEAD5_GIP -.284 .230 -.299 -1.238 .216

LEAD6_GIP .183 .332 .193 .553 .581

LEAD7_GIP -.123 .325 -.129 -.378 .706

LEAD8_GIP .184 .286 .193 .645 .519

LEAD10_GIP .174 .288 .181 .605 .546

LEAD11_GIP -.694 .332 -.719 -2.094 .037

LEAD12_GIP .623 .238 .643 2.617 .009

LAG11_M2 -.104 .065 -.068 -1.593 .112

LAG10_TS .401 .963 .099 .416 .678

LAG11_TS -1.430 1.412 -.353 -1.013 .312

LAG12_TS 1.066 .969 .263 1.100 .272

LEAD1_DHS -.031 .018 -.162 -1.689 .092

LEAD2_DHS .031 .021 .158 1.482 .139

LEAD3_DHS .011 .021 .055 .510 .610

LEAD4_DHS -.016 .021 -.085 -.796 .426

LEAD6_DHS .025 .021 .128 1.198 .231

LEAD8_DHS -.012 .021 -.064 -.586 .558

LEAD9_DHS .018 .021 .091 .853 .394

LEAD11_DHS -.034 .020 -.177 -1.730 .084

LEAD12_DHS .022 .018 .112 1.204 .229

LEAD1_DCC .051 .019 .286 2.706 .007

LEAD2_DCC -.045 .024 -.254 -1.839 .067

LEAD3_DCC .026 .021 .147 1.211 .227

LEAD5_DCC -.012 .015 -.067 -.769 .443

LEAD9_DCC -.010 .020 -.058 -.527 .598

LEAD10_DCC .010 .025 .054 .386 .700

LEAD11_DCC .086 .024 .484 3.513 .000

LEAD12_DCC -.094 .018 -.527 -5.085 .000


Remove LEAD7_GIP

Model Summary

68



Estimate

1 .417a .174 .127

.042218648224332

a. Predictors: (Constant), LEAD12_DCC, LEAD6_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD5_DCC, LEAD1_DHS, LEAD9_DCC, LEAD1_DCC, LEAD12_DHS, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD12_GIP, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD3_DHS, LEAD11_DCC, LEAD10_DCC, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD5_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

ANOVAa


1 Regression .179 27 .007 3.713 .000b

Residual .850 477 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD6_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD5_DCC, LEAD1_DHS, LEAD9_DCC, LEAD1_DCC, LEAD12_DHS, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD12_GIP, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD3_DHS, LEAD11_DCC, LEAD10_DCC, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD5_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.697 .090

LEAD5_GIP -.283 .229 -.297 -1.233 .218

LEAD6_GIP .122 .288 .128 .422 .674

LEAD8_GIP .124 .237 .130 .524 .600

LEAD10_GIP .171 .288 .177 .593 .553

LEAD11_GIP -.699 .331 -.724 -2.111 .035

LEAD12_GIP .629 .237 .650 2.650 .008

LAG11_M2 -.104 .065 -.068 -1.598 .111

LAG10_TS .429 .959 .106 .447 .655

LAG11_TS -1.466 1.408 -.362 -1.041 .298

LAG12_TS 1.073 .968 .265 1.109 .268

69

LEAD1_DHS -.031 .018 -.161 -1.686 .092

LEAD2_DHS .031 .021 .159 1.488 .137

LEAD3_DHS .010 .021 .053 .494 .622

LEAD4_DHS -.016 .021 -.085 -.796 .427

LEAD6_DHS .024 .021 .126 1.183 .238

LEAD8_DHS -.012 .021 -.060 -.557 .578

LEAD9_DHS .018 .021 .092 .862 .389

LEAD11_DHS -.035 .020 -.179 -1.745 .082

LEAD12_DHS .022 .018 .112 1.206 .229

LEAD1_DCC .051 .019 .288 2.725 .007

LEAD2_DCC -.046 .024 -.258 -1.882 .061

LEAD3_DCC .027 .021 .150 1.242 .215

LEAD5_DCC -.012 .015 -.070 -.801 .424

LEAD9_DCC -.010 .020 -.058 -.521 .603

LEAD10_DCC .010 .025 .058 .419 .676

LEAD11_DCC .085 .024 .478 3.495 .001

LEAD12_DCC -.094 .018 -.526 -5.079 .000


Remove LEAD10_DCC

Model Summary



Estimate

1 .416a .173 .128

.042182217247139

a. Predictors: (Constant), LEAD12_DCC, LEAD6_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD5_DCC, LEAD1_DHS, LEAD9_DCC, LEAD1_DCC, LEAD12_DHS, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD12_GIP, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD3_DHS, LEAD11_DCC, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD5_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

ANOVAa


1 Regression .178 26 .007 3.856 .000b

Residual .851 478 .002 Total 1.029 504

70

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD6_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD5_DCC, LEAD1_DHS, LEAD9_DCC, LEAD1_DCC, LEAD12_DHS, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD12_GIP, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD3_DHS, LEAD11_DCC, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD5_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.696 .091

LEAD5_GIP -.286 .229 -.300 -1.246 .213

LEAD6_GIP .127 .288 .133 .441 .659

LEAD8_GIP .117 .236 .122 .493 .622

LEAD10_GIP .178 .287 .185 .619 .536

LEAD11_GIP -.712 .329 -.737 -2.161 .031

LEAD12_GIP .641 .236 .661 2.717 .007

LAG11_M2 -.104 .065 -.068 -1.597 .111

LAG10_TS .492 .947 .122 .520 .603

LAG11_TS -1.568 1.385 -.387 -1.132 .258

LAG12_TS 1.112 .962 .275 1.156 .248

LEAD1_DHS -.030 .018 -.157 -1.654 .099

LEAD2_DHS .030 .021 .156 1.471 .142

LEAD3_DHS .010 .021 .050 .464 .643

LEAD4_DHS -.016 .021 -.084 -.789 .430

LEAD6_DHS .025 .021 .127 1.191 .234

LEAD8_DHS -.012 .021 -.061 -.563 .574

LEAD9_DHS .018 .021 .094 .878 .381

LEAD11_DHS -.034 .020 -.176 -1.727 .085

LEAD12_DHS .021 .018 .109 1.177 .240

LEAD1_DCC .051 .019 .289 2.742 .006

LEAD2_DCC -.046 .024 -.258 -1.880 .061

LEAD3_DCC .027 .021 .150 1.242 .215

LEAD5_DCC -.012 .015 -.070 -.805 .421

LEAD9_DCC -.005 .016 -.029 -.335 .738

LEAD11_DCC .090 .021 .506 4.226 .000

LEAD12_DCC -.094 .018 -.525 -5.078 .000

71


Remove LEAD9_DCC

Model Summary



Estimate

1 .416a .173 .130

.042143108103050

a. Predictors: (Constant), LEAD12_DCC, LEAD6_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD5_DCC, LEAD1_DHS, LEAD1_DCC, LEAD12_DHS, LEAD12_GIP, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD11_DCC, LEAD3_DHS, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD5_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

ANOVAa


1 Regression .178 25 .007 4.013 .000b

Residual .851 479 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD6_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD5_DCC, LEAD1_DHS, LEAD1_DCC, LEAD12_DHS, LEAD12_GIP, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD11_DCC, LEAD3_DHS, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD5_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.736 .083

LEAD5_GIP -.292 .228 -.307 -1.281 .201

LEAD6_GIP .140 .285 .147 .492 .623

LEAD8_GIP .122 .236 .127 .517 .606

LEAD10_GIP .175 .287 .182 .611 .541

LEAD11_GIP -.717 .329 -.743 -2.181 .030

LEAD12_GIP .632 .234 .652 2.699 .007

72

LAG11_M2 -.105 .065 -.068 -1.610 .108

LAG10_TS .473 .944 .117 .501 .617

LAG11_TS -1.585 1.383 -.391 -1.146 .252

LAG12_TS 1.146 .956 .283 1.198 .232

LEAD1_DHS -.030 .018 -.158 -1.664 .097

LEAD2_DHS .031 .020 .159 1.500 .134

LEAD3_DHS .010 .021 .050 .467 .641

LEAD4_DHS -.016 .021 -.085 -.794 .428

LEAD6_DHS .024 .021 .124 1.168 .243

LEAD8_DHS -.012 .021 -.062 -.576 .565

LEAD9_DHS .018 .021 .093 .872 .384

LEAD11_DHS -.034 .020 -.175 -1.714 .087

LEAD12_DHS .022 .018 .111 1.204 .229

LEAD1_DCC .050 .018 .285 2.724 .007

LEAD2_DCC -.046 .024 -.258 -1.880 .061

LEAD3_DCC .027 .021 .152 1.266 .206

LEAD5_DCC -.013 .015 -.076 -.886 .376

LEAD11_DCC .087 .019 .488 4.536 .000

LEAD12_DCC -.093 .018 -.522 -5.077 .000


Remove LEAD3_DHS

Model Summary



Estimate

1 .416a .173 .131

.042108764536307

a. Predictors: (Constant), LEAD12_DCC, LEAD6_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD5_DCC, LEAD1_DHS, LEAD1_DCC, LEAD12_DHS, LEAD12_GIP, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD11_DCC, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD5_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

ANOVAa


1 Regression .178 24 .007 4.178 .000b

Residual .851 480 .002

73

Total 1.029 504 a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD6_GIP, LAG11_M2, LAG10_TS, LEAD8_DHS, LEAD5_DCC, LEAD1_DHS, LEAD1_DCC, LEAD12_DHS, LEAD12_GIP, LEAD4_DHS, LEAD3_DCC, LEAD11_DHS, LEAD6_DHS, LEAD2_DHS, LEAD9_DHS, LEAD11_DCC, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD5_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.735 .083

LEAD5_GIP -.283 .227 -.297 -1.245 .214

LEAD6_GIP .134 .284 .141 .471 .638

LEAD8_GIP .122 .236 .127 .517 .605

LEAD10_GIP .177 .286 .184 .618 .537

LEAD11_GIP -.726 .328 -.752 -2.214 .027

LEAD12_GIP .638 .234 .658 2.731 .007

LAG11_M2 -.105 .065 -.069 -1.613 .107

LAG10_TS .484 .943 .120 .513 .608

LAG11_TS -1.601 1.381 -.396 -1.159 .247

LAG12_TS 1.149 .955 .284 1.202 .230

LEAD1_DHS -.028 .018 -.147 -1.599 .110

LEAD2_DHS .033 .020 .172 1.685 .093

LEAD4_DHS -.014 .020 -.070 -.687 .492

LEAD6_DHS .025 .020 .131 1.243 .214

LEAD8_DHS -.011 .021 -.059 -.549 .583

LEAD9_DHS .018 .021 .095 .895 .371

LEAD11_DHS -.034 .020 -.176 -1.729 .085

LEAD12_DHS .021 .018 .110 1.189 .235

LEAD1_DCC .050 .018 .282 2.705 .007

LEAD2_DCC -.045 .024 -.255 -1.866 .063

LEAD3_DCC .027 .021 .153 1.275 .203

LEAD5_DCC -.013 .015 -.075 -.876 .382

LEAD11_DCC .086 .019 .485 4.518 .000

LEAD12_DCC -.092 .018 -.517 -5.059 .000


74

Remove LEAD6_GIP

Model Summary



Estimate

1 .415a .172 .133

.042074705706002

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG10_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD4_DHS, LEAD11_DHS, LEAD2_DHS, LEAD8_DHS, LEAD11_DCC, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

ANOVAa


1 Regression .177 23 .008 4.357 .000b

Residual .852 481 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG10_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD4_DHS, LEAD11_DHS, LEAD2_DHS, LEAD8_DHS, LEAD11_DCC, LEAD2_DCC, LAG12_TS, LEAD8_GIP, LEAD10_GIP, LEAD11_GIP, LAG11_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.740 .082

LEAD5_GIP -.195 .130 -.205 -1.496 .135

LEAD8_GIP .165 .217 .172 .759 .448

LEAD10_GIP .188 .285 .195 .659 .510

LEAD11_GIP -.735 .327 -.761 -2.248 .025

LEAD12_GIP .637 .233 .657 2.728 .007

LAG11_M2 -.105 .065 -.068 -1.609 .108

LAG10_TS .475 .942 .117 .505 .614

LAG11_TS -1.626 1.379 -.402 -1.179 .239

LAG12_TS 1.184 .952 .292 1.243 .214

75

LEAD1_DHS -.029 .018 -.149 -1.635 .103

LEAD2_DHS .033 .020 .171 1.676 .094

LEAD4_DHS -.012 .019 -.063 -.625 .533

LEAD6_DHS .026 .020 .135 1.286 .199

LEAD8_DHS -.013 .021 -.067 -.628 .531

LEAD9_DHS .018 .021 .095 .896 .371

LEAD11_DHS -.034 .020 -.174 -1.717 .087

LEAD12_DHS .021 .018 .108 1.174 .241

LEAD1_DCC .050 .018 .285 2.738 .006

LEAD2_DCC -.046 .024 -.261 -1.914 .056

LEAD3_DCC .029 .021 .162 1.366 .172

LEAD5_DCC -.014 .015 -.077 -.910 .363

LEAD11_DCC .087 .019 .488 4.556 .000

LEAD12_DCC -.092 .018 -.519 -5.094 .000


Remove LAG10_TS

Model Summary



Estimate

1 .415a .172 .134

.042042169887306

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD4_DHS, LEAD11_DHS, LEAD2_DHS, LEAD8_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD10_GIP, LEAD11_GIP

ANOVAa


1 Regression .177 22 .008 4.551 .000b

Residual .852 482 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD4_DHS, LEAD11_DHS, LEAD2_DHS, LEAD8_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD10_GIP, LEAD11_GIP

76

Coefficientsa

Model



1 (Constant) .010 .006 1.760 .079

LEAD5_GIP -.193 .130 -.203 -1.480 .140

LEAD8_GIP .162 .217 .169 .746 .456

LEAD10_GIP .182 .285 .190 .640 .522

LEAD11_GIP -.738 .327 -.764 -2.258 .024

LEAD12_GIP .645 .233 .666 2.772 .006

LAG11_M2 -.104 .065 -.068 -1.603 .110

LAG11_TS -1.117 .941 -.276 -1.188 .235

LAG12_TS 1.143 .948 .282 1.206 .228

LEAD1_DHS -.029 .018 -.150 -1.644 .101

LEAD2_DHS .033 .020 .171 1.676 .094

LEAD4_DHS -.012 .019 -.063 -.628 .531

LEAD6_DHS .026 .020 .137 1.304 .193

LEAD8_DHS -.013 .020 -.068 -.641 .522

LEAD9_DHS .019 .021 .097 .910 .363

LEAD11_DHS -.034 .020 -.177 -1.746 .082

LEAD12_DHS .021 .018 .109 1.185 .236

LEAD1_DCC .050 .018 .283 2.724 .007

LEAD2_DCC -.046 .024 -.262 -1.921 .055

LEAD3_DCC .029 .021 .164 1.387 .166

LEAD5_DCC -.014 .015 -.076 -.899 .369

LEAD11_DCC .087 .019 .488 4.562 .000

LEAD12_DCC -.092 .018 -.520 -5.099 .000


Remove LEAD4_DHS

Model Summary



Estimate

1 .414a .171 .135

.042015786553964

77

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD2_DHS, LEAD11_DHS, LEAD8_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD10_GIP, LEAD11_GIP

ANOVAa


1 Regression .176 21 .008 4.754 .000b

Residual .853 483 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD2_DHS, LEAD11_DHS, LEAD8_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD10_GIP, LEAD11_GIP

Coefficientsa

Model



1 (Constant) .010 .006 1.759 .079

LEAD5_GIP -.196 .130 -.206 -1.502 .134

LEAD8_GIP .157 .216 .164 .724 .470

LEAD10_GIP .186 .285 .193 .654 .513

LEAD11_GIP -.736 .327 -.762 -2.253 .025

LEAD12_GIP .645 .233 .666 2.774 .006

LAG11_M2 -.104 .065 -.068 -1.600 .110

LAG11_TS -1.091 .939 -.269 -1.162 .246

LAG12_TS 1.119 .946 .276 1.182 .238

LEAD1_DHS -.031 .017 -.160 -1.782 .075

LEAD2_DHS .029 .019 .151 1.559 .120

LEAD6_DHS .022 .019 .116 1.168 .243

LEAD8_DHS -.015 .020 -.077 -.736 .462

LEAD9_DHS .017 .020 .090 .856 .393

LEAD11_DHS -.034 .020 -.175 -1.730 .084

LEAD12_DHS .022 .018 .112 1.214 .226

LEAD1_DCC .051 .018 .288 2.777 .006

78

LEAD2_DCC -.046 .024 -.261 -1.916 .056

LEAD3_DCC .028 .021 .160 1.350 .178

LEAD5_DCC -.014 .015 -.080 -.940 .348

LEAD11_DCC .088 .019 .492 4.613 .000

LEAD12_DCC -.093 .018 -.523 -5.151 .000


Remove LEAD10_GIP

Model Summary



Estimate

1 .413a .171 .136

.041990958662971

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD2_DHS, LEAD11_DHS, LEAD8_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD11_GIP

ANOVAa


1 Regression .176 20 .009 4.977 .000b

Residual .853 484 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD2_DHS, LEAD11_DHS, LEAD8_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD11_GIP

Coefficientsa

Model



1 (Constant) .010 .006 1.744 .082

LEAD5_GIP -.195 .130 -.205 -1.502 .134

LEAD8_GIP .217 .196 .227 1.111 .267

LEAD11_GIP -.616 .271 -.638 -2.278 .023

79

LEAD12_GIP .652 .232 .673 2.809 .005

LAG11_M2 -.103 .065 -.068 -1.597 .111

LAG11_TS -1.119 .937 -.276 -1.193 .233

LAG12_TS 1.151 .944 .284 1.218 .224

LEAD1_DHS -.031 .017 -.159 -1.769 .078

LEAD2_DHS .028 .019 .146 1.515 .130

LEAD6_DHS .022 .019 .115 1.152 .250

LEAD8_DHS -.012 .020 -.064 -.624 .533

LEAD9_DHS .018 .020 .094 .888 .375

LEAD11_DHS -.036 .019 -.185 -1.854 .064

LEAD12_DHS .021 .018 .110 1.193 .234

LEAD1_DCC .051 .018 .287 2.775 .006

LEAD2_DCC -.047 .024 -.268 -1.977 .049

LEAD3_DCC .029 .021 .165 1.401 .162

LEAD5_DCC -.013 .015 -.075 -.886 .376

LEAD11_DCC .088 .019 .493 4.625 .000

LEAD12_DCC -.094 .018 -.529 -5.227 .000


Remove LEAD8_DHS

Model Summary



Estimate

1 .412a .170 .137

.041964530301190

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD2_DHS, LEAD11_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD11_GIP

ANOVAa


1 Regression .175 19 .009 5.225 .000b

Residual .854 485 .002 Total 1.029 504


80

b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD9_DHS, LEAD3_DCC, LEAD2_DHS, LEAD11_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD11_GIP

Coefficientsa

Model



1 (Constant) .010 .006 1.759 .079

LEAD5_GIP -.184 .129 -.193 -1.428 .154

LEAD8_GIP .206 .195 .215 1.058 .291

LEAD11_GIP -.624 .270 -.647 -2.312 .021

LEAD12_GIP .657 .232 .678 2.835 .005

LAG11_M2 -.104 .065 -.068 -1.601 .110

LAG11_TS -1.103 .937 -.273 -1.178 .239

LAG12_TS 1.135 .944 .280 1.202 .230

LEAD1_DHS -.031 .017 -.159 -1.775 .076

LEAD2_DHS .027 .019 .141 1.472 .142

LEAD6_DHS .018 .018 .094 1.004 .316

LEAD9_DHS .014 .019 .071 .715 .475

LEAD11_DHS -.037 .019 -.193 -1.942 .053

LEAD12_DHS .020 .018 .102 1.122 .262

LEAD1_DCC .051 .018 .286 2.762 .006

LEAD2_DCC -.048 .024 -.271 -2.006 .045

LEAD3_DCC .030 .021 .171 1.462 .144

LEAD5_DCC -.013 .015 -.073 -.872 .383

LEAD11_DCC .087 .019 .491 4.611 .000

LEAD12_DCC -.094 .018 -.527 -5.213 .000


Remove LEAD9_DHS

Model Summary



Estimate

1 .411a .169 .138

.041943396093686

81

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD3_DCC, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD11_GIP

ANOVAa


1 Regression .174 18 .010 5.492 .000b

Residual .855 486 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD5_DCC, LEAD1_DHS, LEAD12_GIP, LEAD5_GIP, LEAD3_DCC, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD11_GIP

Coefficientsa

Model



1 (Constant) .010 .006 1.734 .084

LEAD5_GIP -.187 .129 -.197 -1.458 .146

LEAD8_GIP .188 .193 .196 .975 .330

LEAD11_GIP -.575 .261 -.596 -2.204 .028

LEAD12_GIP .634 .229 .654 2.763 .006

LAG11_M2 -.103 .065 -.067 -1.592 .112

LAG11_TS -1.097 .936 -.271 -1.172 .242

LAG12_TS 1.129 .943 .279 1.198 .232

LEAD1_DHS -.031 .017 -.160 -1.786 .075

LEAD2_DHS .028 .019 .145 1.510 .132

LEAD6_DHS .023 .017 .119 1.369 .172

LEAD11_DHS -.033 .018 -.169 -1.808 .071

LEAD12_DHS .023 .017 .118 1.335 .182

LEAD1_DCC .051 .018 .286 2.765 .006

LEAD2_DCC -.048 .024 -.269 -1.992 .047

LEAD3_DCC .031 .021 .174 1.480 .139

LEAD5_DCC -.014 .015 -.080 -.953 .341

82

LEAD11_DCC .087 .019 .491 4.612 .000

LEAD12_DCC -.094 .018 -.528 -5.221 .000


Remove LEAD5_DCC

Model Summary



Estimate

1 .409a .167 .138

.041939416675360

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD5_GIP, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD12_GIP

ANOVAa


1 Regression .172 17 .010 5.763 .000b

Residual .857 487 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD5_GIP, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD8_GIP, LAG12_TS, LEAD12_GIP

Coefficientsa

Model



1 (Constant) .011 .006 1.786 .075

LEAD5_GIP -.152 .123 -.160 -1.234 .218

LEAD8_GIP .140 .186 .146 .751 .453

LEAD11_GIP -.604 .259 -.625 -2.327 .020

LEAD12_GIP .657 .228 .678 2.878 .004

LAG11_M2 -.101 .065 -.066 -1.558 .120

LAG11_TS -1.124 .936 -.278 -1.201 .230

LAG12_TS 1.153 .943 .285 1.223 .222

83

LEAD1_DHS -.031 .017 -.162 -1.805 .072

LEAD2_DHS .028 .019 .144 1.507 .132

LEAD6_DHS .023 .017 .119 1.361 .174

LEAD11_DHS -.032 .018 -.165 -1.769 .077

LEAD12_DHS .022 .017 .113 1.284 .200

LEAD1_DCC .051 .018 .286 2.766 .006

LEAD2_DCC -.046 .024 -.260 -1.932 .054

LEAD3_DCC .022 .019 .125 1.183 .238

LEAD11_DCC .088 .019 .493 4.633 .000

LEAD12_DCC -.096 .018 -.538 -5.356 .000


Remove LEAD8_GIP

Model Summary



Estimate

1 .408a .167 .139

.041920679300328

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD5_GIP, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LAG12_TS, LEAD12_GIP

ANOVAa


1 Regression .171 16 .011 6.093 .000b

Residual .858 488 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD5_GIP, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LAG12_TS, LEAD12_GIP

Coefficientsa

Model



84

1 (Constant) .010 .006 1.766 .078

LEAD5_GIP -.085 .085 -.089 -1.002 .317

LEAD11_GIP -.521 .235 -.540 -2.218 .027

LEAD12_GIP .648 .228 .669 2.846 .005

LAG11_M2 -.100 .065 -.066 -1.550 .122

LAG11_TS -1.125 .935 -.278 -1.203 .230

LAG12_TS 1.158 .942 .286 1.229 .220

LEAD1_DHS -.032 .017 -.168 -1.888 .060

LEAD2_DHS .027 .018 .141 1.476 .141

LEAD6_DHS .027 .016 .141 1.731 .084

LEAD11_DHS -.034 .018 -.176 -1.905 .057

LEAD12_DHS .021 .017 .109 1.238 .216

LEAD1_DCC .049 .018 .277 2.701 .007

LEAD2_DCC -.046 .024 -.262 -1.941 .053

LEAD3_DCC .026 .018 .145 1.421 .156

LEAD11_DCC .085 .019 .480 4.574 .000

LEAD12_DCC -.095 .018 -.535 -5.331 .000


Remove LEAD5_GIP

Model Summary



Estimate

1 .406a .165 .139

.041920851892723

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP, LAG12_TS

ANOVAa


1 Regression .170 15 .011 6.432 .000b

Residual .859 489 .002 Total 1.029 504


85

b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP, LAG12_TS

Coefficientsa

Model



1 (Constant) .010 .006 1.757 .079

LEAD11_GIP -.596 .223 -.617 -2.674 .008

LEAD12_GIP .690 .224 .712 3.078 .002

LAG11_M2 -.108 .064 -.071 -1.687 .092

LAG11_TS -1.060 .933 -.262 -1.136 .256

LAG12_TS 1.057 .937 .261 1.128 .260

LEAD1_DHS -.036 .017 -.185 -2.111 .035

LEAD2_DHS .024 .018 .126 1.337 .182

LEAD6_DHS .030 .015 .157 1.966 .050

LEAD11_DHS -.032 .018 -.164 -1.789 .074

LEAD12_DHS .021 .017 .107 1.213 .226

LEAD1_DCC .045 .018 .256 2.550 .011

LEAD2_DCC -.049 .024 -.274 -2.046 .041

LEAD3_DCC .025 .018 .143 1.407 .160

LEAD11_DCC .090 .018 .506 4.975 .000

LEAD12_DCC -.096 .018 -.540 -5.386 .000


Remove LAG12_TS

Model Summary



Estimate

1 .403a .163 .139

.041932504171139

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

ANOVAa

86


1 Regression .167 14 .012 6.797 .000b

Residual .862 490 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LAG11_TS, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

Coefficientsa

Model



1 (Constant) .011 .006 1.852 .065

LEAD11_GIP -.594 .223 -.615 -2.664 .008

LEAD12_GIP .689 .224 .711 3.073 .002

LAG11_M2 -.109 .064 -.071 -1.702 .089

LAG11_TS -.032 .199 -.008 -.161 .872

LEAD1_DHS -.035 .017 -.181 -2.066 .039

LEAD2_DHS .024 .018 .124 1.317 .188

LEAD6_DHS .031 .015 .159 1.988 .047

LEAD11_DHS -.031 .018 -.160 -1.745 .082

LEAD12_DHS .020 .017 .105 1.192 .234

LEAD1_DCC .046 .018 .262 2.611 .009

LEAD2_DCC -.050 .024 -.284 -2.123 .034

LEAD3_DCC .027 .018 .151 1.489 .137

LEAD11_DCC .090 .018 .508 4.992 .000

LEAD12_DCC -.097 .018 -.546 -5.454 .000


Remove LAG11_TS

Model Summary



Estimate

1 .403a .163 .140

.041890887763675

87

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

ANOVAa


1 Regression .167 13 .013 7.333 .000b

Residual .862 491 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD12_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DHS, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

Coefficientsa

Model



1 (Constant) .010 .005 2.094 .037

LEAD11_GIP -.598 .222 -.619 -2.698 .007

LEAD12_GIP .691 .223 .713 3.093 .002

LAG11_M2 -.109 .064 -.071 -1.703 .089

LEAD1_DHS -.035 .017 -.182 -2.080 .038

LEAD2_DHS .024 .018 .124 1.315 .189

LEAD6_DHS .031 .015 .160 2.000 .046

LEAD11_DHS -.031 .018 -.161 -1.759 .079

LEAD12_DHS .020 .017 .103 1.183 .238

LEAD1_DCC .046 .018 .261 2.609 .009

LEAD2_DCC -.050 .024 -.284 -2.126 .034

LEAD3_DCC .027 .018 .152 1.492 .136

LEAD11_DCC .090 .018 .509 5.008 .000

LEAD12_DCC -.097 .018 -.547 -5.482 .000


Remove LEAD12_DHS

Model Summary

88



Estimate

1 .400a .160 .140

.041907847190415

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

ANOVAa


1 Regression .165 12 .014 7.821 .000b

Residual .864 492 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD2_DHS, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

Coefficientsa

Model



1 (Constant) .011 .005 2.140 .033

LEAD11_GIP -.642 .218 -.665 -2.939 .003

LEAD12_GIP .727 .221 .751 3.287 .001

LAG11_M2 -.111 .064 -.072 -1.730 .084

LEAD1_DHS -.034 .017 -.178 -2.035 .042

LEAD2_DHS .022 .018 .113 1.201 .230

LEAD6_DHS .033 .015 .173 2.190 .029

LEAD11_DHS -.016 .012 -.084 -1.305 .193

LEAD1_DCC .047 .018 .265 2.648 .008

LEAD2_DCC -.051 .024 -.286 -2.135 .033

LEAD3_DCC .027 .018 .153 1.510 .132

LEAD11_DCC .090 .018 .509 5.006 .000

LEAD12_DCC -.096 .018 -.537 -5.400 .000


89

Remove LEAD2_DHS

Model Summary



Estimate

1 .397a .158 .139

.041926669084340

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

ANOVAa


1 Regression .162 11 .015 8.393 .000b

Residual .867 493 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

Coefficientsa

Model



1 (Constant) .010 .005 2.109 .035

LEAD11_GIP -.637 .218 -.660 -2.915 .004

LEAD12_GIP .722 .221 .745 3.260 .001

LAG11_M2 -.109 .064 -.071 -1.705 .089

LEAD1_DHS -.020 .012 -.105 -1.665 .097

LEAD6_DHS .039 .014 .204 2.723 .007

LEAD11_DHS -.017 .012 -.087 -1.362 .174

LEAD1_DCC .048 .018 .271 2.710 .007

LEAD2_DCC -.051 .024 -.286 -2.138 .033

LEAD3_DCC .028 .018 .160 1.578 .115

LEAD11_DCC .090 .018 .505 4.974 .000

LEAD12_DCC -.094 .018 -.531 -5.341 .000


90

RemoveLEAD11_DHS

Model Summary



Estimate

1 .393a .155 .137

.041962973387814

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

ANOVAa


1 Regression .159 10 .016 9.031 .000b

Residual .870 494 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD1_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

Coefficientsa

Model



1 (Constant) .010 .005 2.117 .035

LEAD11_GIP -.573 .214 -.594 -2.684 .008

LEAD12_GIP .656 .216 .677 3.033 .003

LAG11_M2 -.107 .064 -.070 -1.662 .097

LEAD1_DHS -.017 .012 -.088 -1.421 .156

LEAD6_DHS .029 .012 .149 2.359 .019

LEAD1_DCC .047 .018 .263 2.631 .009

LEAD2_DCC -.051 .024 -.286 -2.132 .034

LEAD3_DCC .030 .018 .168 1.659 .098

LEAD11_DCC .088 .018 .497 4.894 .000

LEAD12_DCC -.097 .018 -.548 -5.553 .000


Remove LEAD1_DHS

91

Model Summary



Estimate

1 .389a .151 .136

.042006142878371

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

ANOVAa


1 Regression .155 9 .017 9.790 .000b

Residual .873 495 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP, LEAD3_DCC, LEAD11_DCC, LEAD2_DCC, LEAD12_GIP

Coefficientsa

Model



1 (Constant) .011 .005 2.284 .023

LEAD11_GIP -.605 .213 -.626 -2.842 .005

LEAD12_GIP .683 .216 .705 3.167 .002

LAG11_M2 -.114 .064 -.075 -1.788 .074

LEAD6_DHS .021 .011 .107 1.918 .056

LEAD1_DCC .042 .017 .238 2.416 .016

LEAD2_DCC -.052 .024 -.296 -2.206 .028

LEAD3_DCC .030 .018 .171 1.686 .092

LEAD11_DCC .088 .018 .497 4.891 .000

LEAD12_DCC -.098 .018 -.549 -5.562 .000


Remove LEAD3_DCC

Model Summary

92



Estimate

1 .382a .146 .132

.042084135212625

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP, LEAD2_DCC, LEAD11_DCC, LEAD12_GIP

ANOVAa


1 Regression .150 8 .019 10.619 .000b

Residual .878 496 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP, LEAD2_DCC, LEAD11_DCC, LEAD12_GIP

Coefficientsa

Model


t Sig.



1 (Constant) .011 .005 2.213 .027 LEAD11_GIP -.556 .211 -.575 -2.631 .009 .036 27.778

LEAD12_GIP .656 .215 .677 3.044 .002 .035 28.700

LAG11_M2 -.116 .064 -.076 -1.813 .070 .981 1.020

LEAD6_DHS .022 .011 .113 2.015 .044 .548 1.824

LEAD1_DCC .039 .017 .221 2.248 .025 .179 5.601

LEAD2_DCC -.026 .018 -.145 -1.450 .148 .171 5.850

LEAD11_DCC .087 .018 .489 4.811 .000 .166 6.006

LEAD12_DCC -.094 .017 -.530 -5.396 .000 .178 5.610


Remove LEAD12_GIP

Model Summary



Estimate

93

1 .361a .130 .118

.042432682627422

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP, LEAD2_DCC, LEAD11_DCC

ANOVAa


1 Regression .134 7 .019 10.635 .000b

Residual .895 497 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP, LEAD2_DCC, LEAD11_DCC

Coefficientsa

Model


t Sig.



1 (Constant) .013 .005 2.562 .011 LEAD11_GIP .055 .067 .057 .824 .410 .368 2.719

LAG11_M2 -.129 .065 -.084 -1.995 .047 .985 1.016

LEAD6_DHS .022 .011 .112 1.982 .048 .548 1.824

LEAD1_DCC .038 .018 .216 2.180 .030 .179 5.600

LEAD2_DCC -.028 .018 -.158 -1.565 .118 .171 5.840

LEAD11_DCC .096 .018 .540 5.342 .000 .171 5.843

LEAD12_DCC -.087 .017 -.489 -4.984 .000 .182 5.505


Remove LEAD2_DCC

Model Summary



Estimate

1 .355a .126 .115

.042494365744211

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP, LEAD11_DCC

94

ANOVAa


1 Regression .130 6 .022 11.965 .000b

Residual .899 498 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP, LEAD11_DCC

Coefficientsa

Model


t Sig.



1 (Constant) .013 .005 2.643 .008 LEAD11_GIP .037 .066 .038 .558 .577 .379 2.635

LAG11_M2 -.129 .065 -.084 -1.996 .046 .985 1.016

LEAD6_DHS .021 .011 .110 1.941 .053 .549 1.823

LEAD1_DCC .015 .010 .086 1.586 .113 .591 1.691

LEAD11_DCC .097 .018 .543 5.359 .000 .171 5.842

LEAD12_DCC -.089 .017 -.502 -5.126 .000 .183 5.466


Remove LEAD11_DCC

Model Summary



Estimate

1 .275a .076 .066

.043658516639987

a. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP

ANOVAa


1 Regression .078 5 .016 8.162 .000b

Residual .951 499 .002 Total 1.029 504


95

b. Predictors: (Constant), LEAD12_DCC, LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP

Coefficientsa

Model


t Sig.



1 (Constant) .012 .005 2.318 .021 LEAD11_GIP .115 .066 .119 1.752 .080 .399 2.504

LAG11_M2 -.135 .066 -.088 -2.039 .042 .985 1.015

LEAD6_DHS .020 .011 .105 1.814 .070 .549 1.822

LEAD1_DCC .018 .010 .101 1.800 .072 .593 1.687

LEAD12_DCC -.008 .009 -.044 -.900 .368 .764 1.309


Remove LEAD12_DC

Model Summary



Estimate

1 .272a .074 .067

.043650232484336

a. Predictors: (Constant), LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP

ANOVAa


1 Regression .076 4 .019 10.003 .000b

Residual .953 500 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD1_DCC, LAG11_M2, LEAD6_DHS, LEAD11_GIP

Coefficientsa

Model


t Sig.



1 (Constant) .012 .005 2.284 .023 LEAD11_GIP .101 .064 .105 1.586 .113 .423 2.366

96

LAG11_M2 -.128 .066 -.084 -1.949 .052 .998 1.002

LEAD6_DHS .018 .011 .092 1.643 .101 .585 1.710

LEAD1_DCC .020 .010 .111 2.043 .042 .622 1.609


Remove LEAD11_GIP

Model Summary



Estimate

1 .264a .069 .064

.043716175469415

a. Predictors: (Constant), LEAD1_DCC, LAG11_M2, LEAD6_DHS

ANOVAa


1 Regression .071 3 .024 12.461 .000b

Residual .957 501 .002 Total 1.029 504

a. Dependent Variable: RER b. Predictors: (Constant), LEAD1_DCC, LAG11_M2, LEAD6_DHS

Coefficientsa

Model


t Sig.



1 (Constant) .014 .005 2.904 .004 LAG11_M2 -.130 .066 -.085 -1.964 .050 .998 1.002

LEAD6_DHS .028 .009 .143 3.080 .002 .861 1.161

LEAD1_DCC .028 .008 .157 3.387 .001 .862 1.160


6.6 Assumption Tests with Monthly Dataset

6.6.1 Durbin-Watson Test and Runs Test

Model Summaryb

97



Estimate Durbin-Watson

1 .264a .069 .064

.043716175469415

2.011

a. Predictors: (Constant), LEAD1_DCC, LAG11_M2, LEAD6_DHS b. Dependent Variable: RER

6.6.2 Kolmogorov-Smirnoff Test

One-Sample Kolmogorov-Smirnov Test

Unstandardized

Residual

N 505 Normal Parametersa,b Mean .0000000

Std. Deviation .04358587 Most Extreme Differences Absolute .043

Positive .031 Negative -.043

Test Statistic .043 Asymp. Sig. (2-tailed) .024c

a. Test distribution is Normal. b. Calculated from data. c. Lilliefors Significance Correction.

6.6.3 Glejser Test

Coefficientsa

Model



1 (Constant) .029 .003 9.348 .000

LAG11_M2 .055 .042 .056 1.294 .196

LEAD6_DHS -.002 .006 -.017 -.372 .710

LEAD1_DCC -.026 .005 -.232 -4.969 .000

a. Dependent Variable: RES2

6.6.4 VIF Test

Coefficientsa

98

Model


t Sig.



1 (Constant) .014 .005 2.904 .004 LAG11_M2 -.130 .066 -.085 -1.964 .050 .998 1.002

LEAD6_DHS .028 .009 .143 3.080 .002 .861 1.161

LEAD1_DCC .028 .008 .157 3.387 .001 .862 1.160


Collinearity Diagnosticsa

Model Dimension Eigenvalue Condition Index

Variance Proportions

(Constant) LAG11_M2 LEAD6_DHS LEAD1_DCC

1 1 1.935 1.000 .04 .04 .01 .00

2 1.356 1.195 .00 .00 .30 .32

3 .625 1.760 .00 .00 .69 .68

4 .085 4.780 .96 .96 .00 .00


6.7 Regression with White Standard Error (Monthly Dataset)


HC Method

3

Criterion Variable

RER

Model Fit:

R-sq F df1 df2 p

.0694 8.6633 3.0000 501.0000 .0000



Constant .0140 .0039 3.5985 .0004

LAG11_M2 -.1296 .0540 -2.4024 .0167

LEAD6_DH .0276 .0095 2.9167 .0037

LEAD1_DC .0278 .0094 2.9672 .0031

99

------ END MATRIX -----

6.8 Unit Root Test (Annual Dataset) Null Hypothesis: RER has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=9)

t-Statistic Prob.* Augmented Dickey-Fuller test statistic -5.951779 0.0000

Test critical values: 1% level -3.600987 5% level -2.935001 10% level -2.605836 *MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation Dependent Variable: D(RER) Method: Least Squares Date: 04/15/18 Time: 20:31 Sample (adjusted): 1973 2013 Included observations: 41 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. RER(-1) -0.965875 0.162283 -5.951779 0.0000

C 0.054219 0.030304 1.789160 0.0814 R-squared 0.475973 Mean dependent var 0.003085

Adjusted R-squared 0.462537 S.D. dependent var 0.253820 S.E. of regression 0.186080 Akaike info criterion -0.477727 Sum squared resid 1.350408 Schwarz criterion -0.394138 Log likelihood 11.79341 Hannan-Quinn criter. -0.447289 F-statistic 35.42367 Durbin-Watson stat 1.848896 Prob(F-statistic) 0.000001

Null Hypothesis: LTRIR has a unit root Exogenous: Constant Lag Length: 1 (Automatic - based on SIC, maxlag=9)

100



Augmented Dickey-Fuller Test Equation Dependent Variable: D(LTRIR) Method: Least Squares Date: 04/15/18 Time: 20:33 Sample (adjusted): 1974 2013 Included observations: 40 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. LTRIR(-1) -0.328025 0.124293 -2.639124 0.0121

D(LTRIR(-1)) -0.008640 0.151898 -0.056882 0.9549 C 0.009266 0.004334 2.138051 0.0392 R-squared 0.189350 Mean dependent var 0.000931


Null Hypothesis: M2 has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=9)



101

Augmented Dickey-Fuller Test Equation Dependent Variable: D(M2) Method: Least Squares Date: 04/15/18 Time: 20:34 Sample (adjusted): 1973 2013 Included observations: 41 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. M2(-1) -0.509852 0.131228 -3.885237 0.0004

C 0.031718 0.009410 3.370713 0.0017 R-squared 0.279047 Mean dependent var -0.001614


Null Hypothesis: U3 has a unit root Exogenous: Constant Lag Length: 1 (Automatic - based on SIC, maxlag=9)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(U3) Method: Least Squares Date: 04/15/18 Time: 20:34 Sample (adjusted): 1974 2013 Included observations: 40 after adjustments

102

Variable Coefficient Std. Error t-Statistic Prob. U3(-1) -0.354775 0.101293 -3.502464 0.0012

D(U3(-1)) 0.450478 0.149365 3.015957 0.0046 C 0.023068 0.006705 3.440374 0.0015 R-squared 0.302516 Mean dependent var 0.000375


Null Hypothesis: GIP has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=9)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(GIP) Method: Least Squares Date: 04/15/18 Time: 20:35 Sample (adjusted): 1973 2013 Included observations: 41 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. GIP(-1) -0.880725 0.154031 -5.717842 0.0000


Adjusted R-squared 0.442071 S.D. dependent var 0.060214 S.E. of regression 0.044977 Akaike info criterion -3.317789

103

Sum squared resid 0.078894 Schwarz criterion -3.234200 Log likelihood 70.01468 Hannan-Quinn criter. -3.287351 F-statistic 32.69372 Durbin-Watson stat 1.952287 Prob(F-statistic) 0.000001

Null Hypothesis: DCC has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=9)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(DCC) Method: Least Squares Date: 04/15/18 Time: 20:35 Sample (adjusted): 1973 2013 Included observations: 41 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. DCC(-1) -1.081431 0.159531 -6.778809 0.0000

C -0.010013 0.047436 -0.211078 0.8339 R-squared 0.540919 Mean dependent var -0.000113

Adjusted R-squared 0.529147 S.D. dependent var 0.442433 S.E. of regression 0.303592 Akaike info criterion 0.501284 Sum squared resid 3.594547 Schwarz criterion 0.584873 Log likelihood -8.276320 Hannan-Quinn criter. 0.531722 F-statistic 45.95225 Durbin-Watson stat 1.881382 Prob(F-statistic) 0.000000

Null Hypothesis: DHS has a unit root Exogenous: Constant

104

Lag Length: 0 (Automatic - based on SIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -7.066597 0.0000


Augmented Dickey-Fuller Test Equation Dependent Variable: D(DHS) Method: Least Squares Date: 04/15/18 Time: 20:35 Sample (adjusted): 1973 2013 Included observations: 41 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. DHS(-1) -1.123164 0.158940 -7.066597 0.0000

C -0.027780 0.045127 -0.615605 0.5417 R-squared 0.561486 Mean dependent var 0.000509

Adjusted R-squared 0.550242 S.D. dependent var 0.429162 S.E. of regression 0.287813 Akaike info criterion 0.394539 Sum squared resid 3.230616 Schwarz criterion 0.478128 Log likelihood -6.088043 Hannan-Quinn criter. 0.424977 F-statistic 49.93679 Durbin-Watson stat 1.924909 Prob(F-statistic) 0.000000

Null Hypothesis: RP has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=9)


Test critical values: 1% level -3.600987 5% level -2.935001 10% level -2.605836

105

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation Dependent Variable: D(RP) Method: Least Squares Date: 04/15/18 Time: 20:36 Sample (adjusted): 1973 2013 Included observations: 41 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. RP(-1) -0.748993 0.153068 -4.893218 0.0000

C 0.017607 0.003795 4.639223 0.0000 R-squared 0.380398 Mean dependent var 0.000164


Null Hypothesis: TS has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=9)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(TS) Method: Least Squares Date: 04/15/18 Time: 20:36 Sample (adjusted): 1973 2013 Included observations: 41 after adjustments

106

Variable Coefficient Std. Error t-Statistic Prob. TS(-1) -0.487050 0.139148 -3.500227 0.0012

C 0.000814 0.000298 2.730794 0.0094 R-squared 0.239048 Mean dependent var 4.84E-05

Adjusted R-squared 0.219536 S.D. dependent var 0.001468 S.E. of regression 0.001297 Akaike info criterion -10.41018 Sum squared resid 6.56E-05 Schwarz criterion -10.32659 Log likelihood 215.4086 Hannan-Quinn criter. -10.37974 F-statistic 12.25159 Durbin-Watson stat 1.634702 Prob(F-statistic) 0.001180

6.9 Unit Root Test (Monthly Dataset) Null Hypothesis: RER has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=17)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(RER) Method: Least Squares Date: 04/15/18 Time: 20:37 Sample (adjusted): 1972M02 2014M01 Included observations: 504 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. RER(-1) -0.948605 0.044609 -21.26490 0.0000


Adjusted R-squared 0.472855 S.D. dependent var 0.062267

107

S.E. of regression 0.045209 Akaike info criterion -3.351074 Sum squared resid 1.026022 Schwarz criterion -3.334317 Log likelihood 846.4706 Hannan-Quinn criter. -3.344501 F-statistic 452.1959 Durbin-Watson stat 1.994993 Prob(F-statistic) 0.000000

Null Hypothesis: LTRIR has a unit root Exogenous: Constant Lag Length: 8 (Automatic - based on SIC, maxlag=17)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(LTRIR) Method: Least Squares Date: 04/15/18 Time: 20:38 Sample (adjusted): 1972M10 2014M01 Included observations: 496 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. LTRIR(-1) -0.036306 0.023326 -1.556487 0.1202

D(LTRIR(-1)) -0.409218 0.048674 -8.407393 0.0000 D(LTRIR(-2)) -0.405835 0.051633 -7.859991 0.0000 D(LTRIR(-3)) -0.367954 0.053670 -6.855921 0.0000 D(LTRIR(-4)) -0.249883 0.054495 -4.585442 0.0000 D(LTRIR(-5)) -0.261260 0.054075 -4.831419 0.0000 D(LTRIR(-6)) -0.201280 0.052150 -3.859654 0.0001 D(LTRIR(-7)) -0.111092 0.048896 -2.271987 0.0235 D(LTRIR(-8)) -0.158556 0.044688 -3.548090 0.0004


Adjusted R-squared 0.214246 S.D. dependent var 0.002942 S.E. of regression 0.002608 Akaike info criterion -9.040583

108

Sum squared resid 0.003305 Schwarz criterion -8.955773 Log likelihood 2252.065 Hannan-Quinn criter. -9.007292 F-statistic 15.99647 Durbin-Watson stat 2.022916 Prob(F-statistic) 0.000000

Null Hypothesis: M2 has a unit root Exogenous: Constant Lag Length: 13 (Automatic - based on SIC, maxlag=17)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(M2) Method: Least Squares Date: 04/15/18 Time: 20:38 Sample (adjusted): 1973M03 2014M01 Included observations: 491 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. M2(-1) -0.019492 0.007016 -2.778319 0.0057

D(M2(-1)) 0.473902 0.044359 10.68334 0.0000 D(M2(-2)) -0.048883 0.045896 -1.065082 0.2874 D(M2(-3)) 0.146150 0.045892 3.184631 0.0015 D(M2(-4)) -0.163108 0.046548 -3.504066 0.0005 D(M2(-5)) 0.112370 0.047134 2.384072 0.0175 D(M2(-6)) 0.040813 0.047392 0.861192 0.3896 D(M2(-7)) -0.042597 0.047326 -0.900075 0.3685 D(M2(-8)) 0.039766 0.047351 0.839821 0.4014 D(M2(-9)) 0.057999 0.047174 1.229470 0.2195

D(M2(-10)) 0.009047 0.046540 0.194394 0.8460 D(M2(-11)) -0.054587 0.046259 -1.180038 0.2386 D(M2(-12)) -0.394907 0.046291 -8.530912 0.0000 D(M2(-13)) 0.219673 0.044669 4.917833 0.0000

C 0.001187 0.000489 2.428173 0.0155

109

R-squared 0.391192 Mean dependent var -0.000122


Null Hypothesis: GIP has a unit root Exogenous: Constant Lag Length: 15 (Automatic - based on SIC, maxlag=17)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(GIP) Method: Least Squares Date: 04/15/18 Time: 20:40 Sample (adjusted): 1973M05 2014M01 Included observations: 489 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. GIP(-1) -0.059836 0.011830 -5.057816 0.0000

D(GIP(-1)) 0.252788 0.044610 5.666616 0.0000 D(GIP(-2)) 0.210866 0.046095 4.574593 0.0000 D(GIP(-3)) 0.228495 0.047009 4.860612 0.0000 D(GIP(-4)) 0.076674 0.042315 1.811970 0.0706 D(GIP(-5)) -0.028600 0.042459 -0.673581 0.5009 D(GIP(-6)) -0.017814 0.042569 -0.418485 0.6758 D(GIP(-7)) 0.010062 0.042530 0.236578 0.8131 D(GIP(-8)) 0.050103 0.042445 1.180408 0.2384 D(GIP(-9)) 0.121535 0.042318 2.871952 0.0043

D(GIP(-10)) 0.055410 0.042586 1.301128 0.1938

110

D(GIP(-11)) 0.030673 0.042482 0.722032 0.4706 D(GIP(-12)) -0.435990 0.042400 -10.28272 0.0000 D(GIP(-13)) 0.052948 0.046314 1.143238 0.2535 D(GIP(-14)) 0.072040 0.045819 1.572292 0.1166 D(GIP(-15)) 0.154076 0.045012 3.423008 0.0007



Null Hypothesis: DCC has a unit root Exogenous: Constant Lag Length: 12 (Automatic - based on SIC, maxlag=17)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(DCC) Method: Least Squares Date: 04/15/18 Time: 20:40 Sample (adjusted): 1973M02 2014M01 Included observations: 492 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. DCC(-1) -0.127634 0.026717 -4.777290 0.0000

D(DCC(-1)) 0.098580 0.043970 2.241965 0.0254 D(DCC(-2)) 0.113969 0.044099 2.584395 0.0101 D(DCC(-3)) -0.000562 0.044451 -0.012632 0.9899 D(DCC(-4)) 0.007773 0.044423 0.174976 0.8612

111

D(DCC(-5)) 0.077272 0.044478 1.737323 0.0830 D(DCC(-6)) -0.073887 0.044556 -1.658299 0.0979 D(DCC(-7)) 0.076832 0.043794 1.754403 0.0800 D(DCC(-8)) 0.084415 0.043910 1.922437 0.0551 D(DCC(-9)) 0.094112 0.043731 2.152065 0.0319

D(DCC(-10)) 0.116537 0.043643 2.670251 0.0078 D(DCC(-11)) 0.054883 0.043981 1.247869 0.2127 D(DCC(-12)) -0.303331 0.044071 -6.882792 0.0000

C -0.001054 0.004691 -0.224649 0.8223 R-squared 0.211596 Mean dependent var -5.62E-05


Null Hypothesis: DHS has a unit root Exogenous: Constant Lag Length: 12 (Automatic - based on SIC, maxlag=17)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(DHS) Method: Least Squares Date: 04/15/18 Time: 20:41 Sample (adjusted): 1973M02 2014M01 Included observations: 492 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. DHS(-1) -0.092937 0.025337 -3.668080 0.0003

D(DHS(-1)) -0.266877 0.042778 -6.238655 0.0000

112

D(DHS(-2)) -0.007496 0.044328 -0.169105 0.8658 D(DHS(-3)) 0.081627 0.044184 1.847416 0.0653 D(DHS(-4)) 0.126398 0.044277 2.854714 0.0045 D(DHS(-5)) 0.108119 0.044668 2.420504 0.0159 D(DHS(-6)) 0.088695 0.044934 1.973899 0.0490 D(DHS(-7)) 0.056560 0.045115 1.253686 0.2106 D(DHS(-8)) 0.038879 0.045233 0.859531 0.3905 D(DHS(-9)) 0.042643 0.045199 0.943462 0.3459

D(DHS(-10)) 0.003683 0.045321 0.081262 0.9353 D(DHS(-11)) 0.008783 0.045017 0.195100 0.8454 D(DHS(-12)) -0.418631 0.042180 -9.924794 0.0000

C -0.002143 0.004454 -0.481150 0.6306 R-squared 0.353950 Mean dependent var 4.24E-05


Null Hypothesis: RP has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=17)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(RP) Method: Least Squares Date: 04/15/18 Time: 20:41 Sample (adjusted): 1972M02 2014M01 Included observations: 504 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

113

RP(-1) -0.003708 0.001899 -1.952718 0.0514

C 4.73E-05 3.84E-05 1.231380 0.2188 R-squared 0.007539 Mean dependent var -2.62E-05

Adjusted R-squared 0.005562 S.D. dependent var 0.000171 S.E. of regression 0.000170 Akaike info criterion -14.51328 Sum squared resid 1.46E-05 Schwarz criterion -14.49653 Log likelihood 3659.347 Hannan-Quinn criter. -14.50671 F-statistic 3.813108 Durbin-Watson stat 1.692164 Prob(F-statistic) 0.051408

Null Hypothesis: TS has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=17)



Augmented Dickey-Fuller Test Equation Dependent Variable: D(TS) Method: Least Squares Date: 04/15/18 Time: 20:42 Sample (adjusted): 1972M02 2014M01 Included observations: 504 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. TS(-1) -0.016176 0.008088 -1.999889 0.0461


Adjusted R-squared 0.005928 S.D. dependent var 0.002038 S.E. of regression 0.002032 Akaike info criterion -9.555971 Sum squared resid 0.002072 Schwarz criterion -9.539215 Log likelihood 2410.105 Hannan-Quinn criter. -9.549398

114

F-statistic 3.999558 Durbin-Watson stat 1.795154 Prob(F-statistic) 0.046051

6.10 Univariate Regression (Annual Dataset) 6.10.1 DHS

Model Summary



Estimate

1 .585a .342 .326

.149445733679315

a. Predictors: (Constant), DHS

Coefficientsa

Model



1 (Constant) .067 .023 2.899 .006

DHS .377 .083 .585 4.564 .000


6.10.2 DCC

Model Summary



Estimate

1 .639a .409 .394

.141683878132984

a. Predictors: (Constant), DCC

Coefficientsa

Model



1 (Constant) .059 .022 2.681 .011

DCC .386 .073 .639 5.261 .000


6.10.3 RP

115

Model Summary



Estimate

1 .452a .205 .185

.164365712436190

a. Predictors: (Constant), RP

Coefficientsa

Model



1 (Constant) .283 .075 3.794 .000

RP -9.669 3.015 -.452 -3.207 .003


6.10.4 GIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .500a .250 .231

.159647892547

559

a. Predictors: (Constant), GIP

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients


1 (Constant) .015 .027 .548 .586

GIP 1.994 .547 .500 3.647 .001


116

6.10.5 LEAD_GIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .323a .104 .082

.174418264105

058

a. Predictors: (Constant), LEAD_GIP

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .031 .030 1.058 .296

LEAD_GIP 1.329 .616 .323 2.158 .037


6.10.6 LEAD_U3

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .359a .129 .107

.172018101163

805

a. Predictors: (Constant), LEAD_U3

Coefficientsa

117

Model


Standardized

Coefficients


1 (Constant) .334 .116 2.865 .007

LEAD_U3 -4.255 1.750 -.359 -2.431 .020


6.11. Univariate Regression (Monthly Dataset) 6.11.1 Lag11_M2

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .087a .008 .006

.045055072679

613

a. Predictors: (Constant), LAG11_M2

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .014 .005 2.754 .006

LAG11_M2 -.134 .068 -.087 -1.965 .050


6.11.2 LEAD6_DHS

Model Summary

118

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .204a .042 .040

.044276564533

500

a. Predictors: (Constant), LEAD6_DHS

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .006 .002 2.799 .005

LEAD6_DHS .039 .008 .204 4.674 .000


6.11.3 LEAD1_DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .209a .044 .042

.044228085331

075

a. Predictors: (Constant), LEAD1_DCC

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.438 .015

119

LEAD1_DCC .037 .008 .209 4.795 .000


6.11.4 LEAD5_GIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .100a .010 .008

.045001911052

711

a. Predictors: (Constant), LEAD5_GIP

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .003 .002 1.198 .232

LEAD5_GIP .095 .042 .100 2.249 .025


6.11.5 LEAD6_GIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .137a .019 .017

.044799285224

474


120

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .002 .002 .847 .397

LEAD6_GIP .131 .042 .137 3.109 .002


6.11.6 LEAD7_GIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .159a .025 .023

.044655592273

914


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .001 .002 .651 .515

LEAD7_GIP .151 .042 .159 3.602 .000


6.11.7 LEAD8_GIP

121

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .185a .034 .032

.044449599392

850


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .001 .002 .409 .682

LEAD8_GIP .177 .042 .185 4.215 .000


6.11.8 LEAD9_GIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .211a .044 .043

.044210781379

911


Coefficientsa

Model


Standardized

Coefficients


122

1 (Constant) .000 .002 .166 .868

LEAD9_GIP .202 .042 .211 4.838 .000


6.11.9 LEAD10_GIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .229a .052 .050

.044028847692

800


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 4.042E-6 .002 .002 .999

LEAD10_GIP .220 .042 .229 5.269 .000


6.11.10 LEAD11_GIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .234a .055 .053

.043968205326

266

123


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) -9.471E-5 .002 -.044 .965

LEAD11_GIP .226 .042 .234 5.406 .000


6.11.11 LEAD12_GIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .256a .065 .064

.043722028172

837


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) -.001 .002 -.243 .808

LEAD12_GIP .248 .042 .256 5.936 .000


6.11.12 LEAD1_DHS

124

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .129a .017 .015

.044847306587

429


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.614 .009

LEAD1_DHS .025 .009 .129 2.927 .004


6.11.13 LEAD2_DHS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .173a .030 .028

.044545172063

357


Coefficientsa

Model


Standardized

Coefficients


125

1 (Constant) .005 .002 2.725 .007

LEAD2_DHS .033 .008 .173 3.941 .000


6.11.14 LEAD3_DHS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .163a .026 .025

.044625256831

367


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.699 .007

LEAD3_DHS .031 .009 .163 3.698 .000


6.11.15 LEAD4_DHS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .162a .026 .024

.044627479379

196

126


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.700 .007

LEAD4_DHS .031 .009 .162 3.691 .000


6.11.16 LEAD5_DHS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .176a .031 .029

.044524351197

032


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.732 .007

LEAD5_DHS .034 .008 .176 4.002 .000


6.11.17 LEAD7_DHS

127

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .200a .040 .038

.044316141072

759


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .006 .002 2.788 .006

LEAD7_DHS .039 .008 .200 4.572 .000


6.11.18 LEAD8_DHS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .183a .033 .032

.044464181327

793


Coefficientsa

Model


Standardized

Coefficients


128

1 (Constant) .005 .002 2.745 .006

LEAD8_DHS .035 .008 .183 4.174 .000


6.11.19 LEAD9_DHS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .183a .034 .032

.044462512078

964


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.746 .006

LEAD9_DHS .035 .008 .183 4.179 .000


6.11.20 LEAD10_DHS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .171a .029 .027

.044559183429

550

129


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.725 .007

LEAD10_DHS .033 .008 .171 3.899 .000


6.11.21 LEAD11_DHS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .122a .015 .013

.044891931153

195


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.609 .009

LEAD11_DHS .024 .009 .122 2.748 .006


6.11.22 LEAD12_DHS

130

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .132a .017 .015

.044832221523

639


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.628 .009

LEAD12_DHS .025 .009 .132 2.986 .003


6.11.23 LEAD2_DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .177a .031 .029

.044516245670

652


Coefficientsa

Model


Standardized

Coefficients


131

1 (Constant) .005 .002 2.419 .016

LEAD2_DCC .031 .008 .177 4.026 .000


6.11.24 LEAD3_DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .195a .038 .036

.044358256896

130


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.434 .015

LEAD3_DCC .035 .008 .195 4.462 .000


6.11.25 LEAD4_DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .175a .031 .029

.044527222151

059

132


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.428 .016

LEAD4_DCC .031 .008 .175 3.994 .000


6.11.26 LEAD5_DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .173a .030 .028

.044549611927

975


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.429 .015

LEAD5_DCC .031 .008 .173 3.928 .000


6.11.27 LEAD6_DCC

133

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .195a .038 .036

.044363694029

830


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.445 .015

LEAD6_DCC .035 .008 .195 4.448 .000


6.11.28 LEAD7_DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .196a .038 .036

.044352625211

013


Coefficientsa

Model


Standardized

Coefficients


134

1 (Constant) .005 .002 2.450 .015

LEAD7_DCC .035 .008 .196 4.477 .000


6.11.29 LEAD8_DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .206a .042 .041

.044256940317

651


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.457 .014

LEAD8_DCC .037 .008 .206 4.723 .000


6.11.30 LEAD9_DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .211a .044 .043

.044211496812

752

135


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.464 .014

LEAD9_DCC .037 .008 .211 4.836 .000


6.11.31 LEAD10_DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .209a .044 .042

.044224647630

403


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.469 .014

LEAD10_DCC .037 .008 .209 4.804 .000


6.11.32 LEAD11_DCC

136

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .183a .033 .031

.044465993234

125


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .005 .002 2.450 .015

LEAD11_DCC .033 .008 .183 4.169 .000


6.11.33 DCC

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .170a .029 .027

.044570315614

119

a. Predictors: (Constant), DCC

Coefficientsa

Model


Standardized

Coefficients


137

1 (Constant) .005 .002 2.418 .016

DCC .030 .008 .170 3.866 .000


6.11.34 LAG10_TS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .093a .009 .007

.045031928639

417

a. Predictors: (Constant), LAG10_TS

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) -.001 .004 -.391 .696

LAG10_TS .376 .180 .093 2.094 .037


6.11.35 LAG11_TS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .092a .009 .007

.045035026754

215

138


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) -.001 .004 -.377 .706

LAG11_TS .373 .180 .092 2.077 .038


6.11.36 LAG12_TS

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .106a .011 .009

.044971887764

767


Coefficientsa

Model


Standardized

Coefficients


1 (Constant) -.002 .004 -.638 .524

LAG12_TS .430 .179 .106 2.396 .017


Documents

Macroeconomic Variables as Predictors of US …...equity prices, with industrial production relates to real stock returns in an increasingly important manner over time, as compared