Capstone Paper - Final version

Oil Price and Norwegian Foreign Exchange Rate

Empirical analysis: 1999-2015

MATHIAS FJELDAVLIE MUNKEJORD

PACIFIC LUTHERAN UNIVERSITY

ECON 499 – FALL 2015

Oil Price and Norwegian FX Rate Mathias Fjeldavlie Munkejord

Introduction:

Oil is the most important natural resource in the global economy for both individuals and

businesses. The recent decrease in oil prices has significantly impacted the global economy, and

some countries that depend on petroleum exports hurt more than others and has experienced

currency depreciation as a result of the recent oil price plunge. Norway is a good example of this

reality, where the Norwegian citizens complain because the Norwegian Krone is at its weakest level

against the U.S. Dollar since 2002, which makes American products almost 30% more expensive

compared to the previous decade. For my capstone project I want to look at the relationship

between the oil price and the Norwegian exchange rate as it has never been more relevant seen

from a Norwegian economic perspective. The oil sector is facing tough times with substantial job

cutbacks and with 2016 being the first year ever where the Norwegian government has to sell

assets in order to finance next years’ national budget.

I will look at historical data from 1999-2015 to see if I can find a significant correlation

between the oil price and the currency value in a commodity-dependent economy such as Norway.

The specific question I will address is: “How does fluctuations in oil price correlate with the

Norwegian Krone´s performance in the foreign exchange market.” My dataset will include oil

prices, exchange rates between the Krone USD, GBP, SEK (Swedish Krone), and corresponding

output data. The Norwegian Krone was pegged against the Deutsche Mark and other major

currencies until 1999(Kleivset, 2012), and therefore data before 1999 will be excluded from my

research, and I will only focus on the period where the Krone has been a floating currency.

Based on theory and econometric analysis developed by the Norwegian economist Akram

(2004), I will examine the relationship. Even though Akram (2004) only could find a significant

relationship between oil prices and exchange rates when oil price was below 14USD per barrel,

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newer data could lead to a different result. If a statistically significant relationship between oil

prices and the Norwegian Krone can be established, it will generate an interesting basis for

discussing how monetary policy makers in Norway can approach the issue of a downswing in the

economy caused by fluctuations in a commodity price. As of today over 30,000 jobs have been cut

in the Norwegian oil sector alone, and it will be interesting to see if this reality will disperse into

other sectors in the Norwegian economy. After many years with substantial growth in the oil

sector, Norway is now facing a new situation where the greatest contributing sector to the national

GDP has to be restructured.

2.1 – Norwegian economic development:

The Norwegian economy lagged far behind neighboring countries for a long time

period after they claimed independence from Sweden in 1905. However, over the following

100 years, the economic growth in Norway was nothing short of incredible. If we compare

the per capita GDP growth in Norway to the other countries used in my research, we see

that Norway was lagging behind until it discovered oil in 1969. Graph 1 presents the

substantial economic boom that was generated by the increase in oil production, which

shaped the Norwegian economy to be one of the wealthiest countries in the world. As of

2010 the Norwegian economy was the second best performing in the world, only behind

Luxembourg on per capita GDP rankings.

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Value added in Norway, measured as change in Domestic Product (GDP) has

increased by about 90 percent through the 1980s and 1990s, while by comparing with the

the EU, they experienced around 50 per cent growth, and in the USA about 70 percent over

the same period. The strong growth in Norway must be viewed, among other factors, in

connection with the development of the oil industry after the initial discovery of oil on the

Norwegian shelf at the end of the 1960s. From 1980 to 2000, oil production in terms of

number of barrels produced more than tippled, and Norway is now one of the world´s

biggest exporters of oil (Statistics Norway). At the same time the oil sectors demand for

goods and services from the mainland economy has grown substantially.

In a paper issued by the Norwegian government, Sigrid Russwurm (2001) explains

some of the most important underlying factors for the economic success Norway has

experienced in the modern era. She concludes that an important factor behind the economic

development in Norway has been due to the rapid growth in the oil sector. Erling Steigum

(2010) shares Russwurm´s (2001) view on Norwegian economic development. He also adds

that; “Wealth is a combination of luck and ability”. Even though the economy has grown

rapidly since the discovery of oil, it has also faced some issues regarding high unemployment

rates and inflation during the 1980´s and 1990´s. the Norwegian government in consultation

with central policy makers has been smart and proactive in Norway. This resulted in the

establishment of the Norwegian oil fund in the mid 1990´s. The first deposit was made to the

fund in 1996, and since then has the oil fund grown to the largest of its kind. The Norwegian

oil fund was in 2015 worth around $900 billion, which is placed in different financial and

non-financial assets around the world in order to spread risk.

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Norway has an open economy, with a per capita foreign trade that is one of the

highest in the world, in which 77 percent of the exports go to EU countries. The Nordic

countries, Great Britain and Germany are Norway's most important trading partners mostly

because Great Britain and Germany are major markets for Norwegian oil and gas (Statistics

Norway). Sweden is the country that Norway imports the most from. Exports of goods and

services accounted for 46 percent of the GDP in 2000, while imports accounted 31 percent.

Exports of oil and gas constituted 46 per cent of total exports (Statistics Norway). United

Kingdom, United States and Sweden are all countries closely tied to Norway, and, thus, will

be the countries I will focus on in my research and analysis.

2.2 – Commodity Dependent Economies:

Some countries depend on a commodity in their economy. Norway is one of them,

with a significant percentage of GDP coming from the oil-producing sector. A commodity-

dependent economy can be defined as a country in which the economy is highly dependent

on profits from producing and exporting this commodity. Hence, for a country that depends

on a commodity the price and value of that commodity will have great effects on the

financial results from that sector and on the national budget. Céspedes and Velasco (2012)

investigate how fluctuations in commodity prices affect macroeconomic performance, and

conclude that fluctuations in commodity prices for a country that depend on this commodity

often experience macroeconomic volatility. The monetary policy regime can play a crucial

role in terms of controlling the volatility related to commodity price fluctuations. Céspedes

and Velasco (2012) find that more flexible exchange rate regimes such as inflation-targeting,

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tend to help reduce macroeconomic volatility in commodity exporting countries. We see

that Norway in 1999 implemented a flexible exchange rate regime with inflation-targeting

after a period with pegged exchange rate to the major traded currencies. This might have

been implemented in order to secure stable returns from the substantial increase in oil

production in Norway at the time.

With the oil production alone accounting for almost one fourth of the total value

creation in Norway as of 2012 it is easy to understand that a decrease in the oil price could

result in substantial decrease in terms of profits in the economy. (Norges Bank)

The recent drop in global oil prices has already started to raise fear in the Norwegian oil

sector, and projections show that oil prices could stay at relatively low levels for a longer

time period than first anticipated (Statistics Norway). Moderate economic performance

combined with low oil prices could mean that Norway are facing an uncertain future.

Nordbø and Stensland (2015) address this issue in their recent working paper, where they

claim that a decrease in oil price could result in substantial decreases in revenue for the oil-

producing sector, but that the effects also will disperse into all companies delivering

products or services to the oil sector. As of 2014 around 300,000 jobs were related to the oil

activities in Norway, which is a 100% increase since 2000. (Statistics Norway)

With these facts stated we can derive the conclusion that Norway´s economy is

highly dependent on the commodity it produces, and further I will discuss how the exchange

rate also is relevant in this issue.

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2.3 – Exchange rate determination:

When working with exchange rates, it is important to understand how exchange

rates are calculated. Two models have been considered for my research, namely the

monetary approach and the asset approach for exchange rate determination. The monetary

approach uses domestic and foreign monetary variables such as money supply and output,

assuming that the Purchasing Power Parity (PPP) condition holds. The asset approach is built

on the uncovered interest rate parity (UIP), and is more focused on interest rates in relation

to the exchange rate.

The monetary approach is one of the most widely used models over the past decade.

Meese & Rogoff (1988) were one of the first to estimate and test the robustness of this

approach for determining exchange rates. Their research presented that both real exchange

rate and real interest rates had the theoretically anticipated signs, which means that as the

real interest rate increases one could expect the real exchange rate to appreciate. However,

the relationships did not turn out statistically significant, so the model they used would not

be a good reference for forecasting real exchange rates.

After the publication of Meese & Rogoff (1988), a lot of research has been done on

the topic of exchange rates, and how they respond to different variables in the economy.

The importance of exchange rates is especially visible when looking at countries that engage

in international trade because a depreciation of the home currency could impact the

domestic balance of payments. The domestic current account is especially vulnerable in such

a situation because importing foreign goods is more expensive than before.

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The monetary approach is widely used, and Rapach & Wohar (2002) utilized this

approach with data from 14 countries and related monetary data. They found that

monetary variables could explain fluctuations in the exchange rate, a result that is supported

by Akram (2004) in his working paper for the Norwegian Central Bank. My approach will

deviate a bit from the models used in these papers, as I will include oil price as a monetary

variable, and focus mostly on the impact this variable has on the exchange rate. Farooq

Akram has been studying the relationship between the exchange rate and oil price

extensively, and in 2000 he published a paper that found a significant relationship when the

oil price was below USD 14 and falling. He concluded that the relationship was non-linear,

and as the price of oil increased the oil price had an insignificant impact on the exchange

rate.

The interest rate differential and its impact on exchange rates is another way of

examining the relationships between economic variables and fluctuations in the value of

currencies. This approach has been taken by Norwegian economists such as Bjørk, Mork &

Uppstad (1998) and Kloster, Lokshall & Røisland (2007). They all found that the interest rate

differential was a key factor behind both appreciations and depreciations in the exchange

rate. They also found a significant tendency for a long- term decrease in the oil price to

result in a depreciation of the Norwegian krone exchange rate.

For commodity dependent economies, such as Norway with its significant oil

reserves, the exchange rate is especially important since fluctuations in the exchange rate

will impact the national economy in a significant way. Korhonen and Juurikkala (2007)

explore the factors behind equilibrium exchange rates in oil-dependent countries. Their

research presents another way to look for the relationship between exported commodity

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and the real exchange rate. The real exchange rate (REER) can be defined as the nominal

exchange rate adjusted for price level differences between countries, where an increase in

the real exchange rate indicates a depreciated currency. Their econometric analysis of REER

in oil-producing economies finds a statistically significant positive effect on the real exchange

rate of these countries. The real exchange rate depends on macroeconomic variables, and

their conclusion states that the oil price drives many of these variables. Even though the

relationship between oil prices and exchange rates in oil producing economies seems to be

significant, no one has been able to present a clear linear relationship. However, Korhonen

and Juurikkala´s (2007) conclusion is supported by the findings of Zalduendo (2006) for

estimates in Venezuela, and Kalcheva and Oomes (2007) for estimates in Russia.

2.4 – Oil Price:

The oil price is an important indicator of economic well-being in both the Norwegian

economy and the global economy in general. Lizardo and Mollick (2010) claims that the oil

price has to be blamed for economic recessions, financial crisis, increasing unemployment

rates and high inflation. Support of these claims can be found by looking at seminal papers

addressing these issues. Hamilton (1983) researched how oil was related to the American

economy and how oil price shocks have explained recessions where he found the price of oil

to be a significant contributing factor. Burbridge & Harrison (1984) did research on the

correlation between oil prices and price levels in the U.S., and concluded that the increases

in oil price was a driving factor behind the increased U.S. price level during 1970´s. Their

research also claims that price of oil influences the level of industrial production in countries

like U.S. and U.K. Research by Gisser and Goodwin (1986) and Louganini (1986) supports the

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conclusions by Hamilton, Burbridge & Harrison, and further adds that the oil price has both

inflationary effects, and to some extent can explain variations in unemployment.

As explained above the petroleum prices has been researched extensively in

connection with important economic factors. The question that needs to be raised now is to

what extent the oil price can explain fluctuations in the value of currencies. Lizardo & Mollick

(2010) emphasizes that the link between currencies and oil price did not received a lot of

attention in published economic research, but some important studies exist and will be

discussed in the following section.

Golub (1983) and Krugman (1980) did both find that oil- exporting countries could

expect an appreciation of their currency when the oil price increased. Blomberg & Harris

(1995) argue that since crude oil is traded in USD regardless of where in the world it is

traded, a depreciation of the USD would increase purchasing power for international traders

(players) and increase the demand for oil. This would again lead to an increase in the price of

oil based on simple supply and demand theory.

Lizardo & Mollick (2010) provided evidence on how an increase in oil price led to

exchange rate appreciation for oil-exporting countries such as Russia, Canada and Mexico,

while it resulted in exchange rate depreciation for oil-importing countries such as Japan.

Further Lizardo & Mollick (2010) found that countries that had no exposure to oil trading,

such as Great Britain, experienced an appreciation of their exchange rate on the USD when

the oil price increased.

Norwegian economists also addressed the importance of oil price on financial

markets and currency valuation. Bjørk, Mork & Uppstad (1998) approached the issue by

using co-integration analysis combined with an error correction model in order to capture

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long term effects of permanent oil price changes and the consequences of short term

fluctuations in the oil market. The monetary policy in Norway at that time was focusing on

keeping a relatively stable exchange rate, hence did they also focused on the interest rate

differential as an instrument used by the Norwegian central bank to stabilize the Norwegian

krone. Farooq Akram (2000) claims that a number of arguments can be put forward to

explain why the nominal exchange rate of an oil producing country may appreciate when oil

price rises, and vice versa. He especially emphasizes that higher oil prices could increase the

demand for the currency of an oil exporting country, resulting in an appreciation of that

currency relative to other currencies. These claims are supported by the economist

discussed earlier in this section.

The recent drop in oil prices is not an uncommon phenomenon when looking at the

past 15 years. Historical data tells us that oil prices has been trough multiple booms and

busts. The recent plunge in the global oil price can be explained by multiple factors, and

analysts agree that the oil market as of today is oversupplied. One of the greatest reasons

for this reality is OPECs unwillingness to stabilize the market and cut back on the production.

It does not help that china as the worlds largest oil importer might be in a way worse state

than predicted, and that Russia still keeps their pumps going despite the economic downturn

the country is facing. Graph 2 present the fluctuations in the oil price over the last 15 years.

Graph 2: Oil Price from 2000-2015 (source: research.stlousfed.org)

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My literature review reveals that a lot of research has been done on explaining how

monetary variables relates to fluctuations in financial sectors of the economy. Existing

results seem to have a certain common feature, namely that monetary variables and

monetary policy does influence the exchange rate. There are multiple approaches that can

be used in determining the exchange rate, but the one I will focus on is the monetary

approach, where the most important trading partners for Norway will be included as well as

the oil price. If a significant relationship can be established between the two, this paper can

contribute in explaining the recent depreciation of the Norwegian Krone.

3.1 – Model:

My research is focused on whether I can find a correlation between the oil price and

the Norwegian exchange rate. In order to do so I will use the monetary approach to

exchange rate. The monetary approach to exchange rates has been the dominant macro

model after the collapse of the Bretton Woods system, and is also a fairly easy approach to

understand. It conceives the exchange rate as the relative price of two monies, where the

price is turned into a function of the relative supply and demand for those monies (Lizardo &

Mollick, 2010).

A few assumptions have to hold under the monetary approach. The time period is

assumed to be long enough for full price adjustment and that the absolute purchasing power

parity (PPP) holds. The concept of PPP explains the theory that any given commodity will

have the same price anywhere in the world when measured in the same currency. This is

also referred to as the law of one price (LOOP), a theory many believe operates if markets

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are working well both nationally and internationally (Appleyard, Field &Cobb 2010). For

example, if a loaf of bread costs $4,50 in the U.S and £3 in the United Kingdom, then the

exchange rate should be equal to $4,50 divided by £3, or $1,50/£. The PPP can also be

generalized over many goods and be written as:

PPP Absolute=Price LevelUS

Price LevelUK(1)

The monetary approach focuses on monetary variables such as money supply and

national output. The equation for money supply in the home country, given that PPP holds

can be written:

MSa=K a Pa Y a(2)

Where MSa= money supply in country A, Pa= Price level in country A, Ya= Real income

in country A, and Ka= a constant term including all other influences on money demand in

country A in addition to Pa and Ya.

Since the absolute version of PPP is assumed to hold in this model, we end up with an

equation that has good explanatory capabilities between the monetary variables and the

exchange rate, E. Equation (3) explains the impact on changes in monetary variables for both

the home country and the foreign country:

E=K bY b MSa

K aY a MSb(3)

In equation 3, the variable E represent the exchange rate, K reflects sensitivity of real

money demand to income. Where a $5 increase in real income will raise real money demand

by K*5 real dollars. K should be less then one in most cases. Y is the real income in country A

and B, and MS is the money supply in country A and B respectively. The model above

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explains a specific relationship between the variables and the exchange rate E. If money

supply in the home country increases by 10%, holding all else constant, the exchange rate

will increase by 10%. A 10% increase in the exchange rate tells us that it takes 10% more

units of currency A to buy one unit of currency B. An increase in the exchange rate A/B will

therefore be a depreciation of currency A. The relationship works backwards for money

supply in the foreign country. A 10% increase in money supply in country B, ceteris paribus,

will lead to a proportional fall in E, or an appreciation of the home currency.

From these relationships we can se that the monetary approach is focused on

changes in relative money supply, and we can also derive that if real income in country A

increases, their currency will appreciate. So a faster growing economy will experience an

appreciation of the currency. After some rearranging, we derive a simpler expression of the

monetary approach that can be written as:

E=

MSNOR

K NOR∗GDPNOR∗KUS∗GDPUS

MSUS∗Oil Price(4)

Where NOR and US stand for Norway and United States, respectively.

Equation (4) explains the absolute relationship between the variables included in the

monetary approach. For my research I will look at the money supply and output differentials

for Norway and the foreign countries, hence I will adjust the model further by taking the

natural logarithm of all the variables including the oil price that was added in equation (4) by

multiplication, an approach used by Lizardo & Mollick (2010). When taking the natural

logarithm of equation (4) the following equation is generated:

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e=lnMSNOR−(lnK NOR+ lnGDPNOR )+(lnK US+lnGDPUS )−lnMSUS+logOil price(5)

The variable K is a constant rather than a time variant in this model, which means

that it cannot be included in the estimation equation as I work with time series data in this

capstone paper. When K is dropped from the model, and the model is rearranged further in

order to estimate the money supply and output differential for home and foreign country,

estimation equation (6) is generated. Equation (6) is a replica of the one used by by Lizardo &

Mollick (2010). Their composite model was also aimed to test the relationship my paper

intends to, however, their dataset consisted of a different time period as well as different

countries.

The benefit of having the estimation equation in logarithmic form is that the results

can be interpreted as percentage change, such as a 1% increase in the oil price will

correspond in a certain percentage change in the exchange rate, e. When rearranging

equation (5) with the aim of estimating the relationship between money supply differential,

output differential and oil price on the foreign exchange rate we derive the following

estimation equation:

e=β 0+β 1¿

Equation (6) presents (m-m*) as the money supply differential in logarithmic form,

(y-y*) is the output differential in logarithmic form, logOil is the logarithm of the real oil

price and e is the logarithm of the nominal exchange rate. For the money supply I will use

M1 for each country, and for the output I will use industrial production. The oil price is the

price for one barrel of crude oil as a monthly average. As the price of oil increases, and the

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price of oil is denominated in US dollars, the US money supply will increase relative to the

Norwegian money supply. This will, as explained above, result in a depreciation of the US

dollar relative to the Norwegian krone. Lizardo & Mollick (2010) found that the β3 should be

positive for oil exporting countries since an increase in e means a depreciation of the USD

relative to the foreign currency. Hence, more USD has to be paid for each barrel of oil.

However, in my research I am using the Norwegian Krone as the benchmark currency, hence

I am expecting a negative coefficient on the oil price as this would mean that an increase in

oil price would result in an appreciation of the Norwegian Krone relative to foreign

currencies.

By

looking at graph 3 one could say that the NOK/USD exchange rate and the oil price seems to

have a perfectly negative relationship, where a low oil price is consistent with a high

Graph 3: Oil Price and NOK/USD exchange rate 1999-2015 (research.stlousfed.org)

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Conclusions from earlier studies of the relationship are split, but I hope that a

significant relationship can be established for the relationship by utilizing the monetary

approach and also incorporate the oil price.

3.2: DATA

The data I will use in order to test my model is gathered from St. Louis Federal

Reserve (also called FRED) and involve the money supply from Norway, United States,

Sweden, Denmark and Germany(euro-zone) together with industrial production from the

same countries and for the oil price I will use the price per barrel of crude oil as mentioned

earlier. Before I start to run regressions for analytical purposes I have to modify the variables

slightly in order to run the regression as stated in the model section.

I will have to generate new variables that represents the logarithmic differential

between the money supply and industrial output for Norway and the foreign countries.

(m-m*) and (y-y*) respectively. I will also generate logarithmic variables for the exchange

rates and the oil price. I do this in order to get more normally distributed variables, and it

also enables me to see the percentage change impact on the different variables. When

expressed in logarithmic form, a 1% change in for example the money supply differential will

increase or decrease the exchange rate by a certain percentage given by the coefficient on

the specific variable.

The following table presents some descriptive statistics from my time series dataset.

All variables are observed monthly from 1999-2015:

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Data retrieved from: https://research.stlouisfed.org

By looking at the minimum and maximum values from the descriptive statistics table

above, we can observe that both exchange rates and oil price has experienced major

fluctuations over the 15-year time period I am focusing on in my paper. For additional

descriptive statistics, see the appendix.

3.3 – Initial Results:

When I ran the first regression of the monetary approach, these initial results were

generated for the relationship of NOK/USD, NOK/SEK and NOK/GBP exchange rates and the

relevant independent variables:

Variables: NOK/USD NOK/SEK NOK/GBPMoney Supply -0,1130645 -0,2466841 0,4521856

Output -0,1044759 -0,205999 -0,1111683

Oil Price -0,2691326 -0,0265537 -0,1873946

Constant 5,215184 4,421817 3,286315

R2 0,8226 0,1395 0,683

Adj. R2 0,8197 0,1293 0,6778

Observations 186 186 186

The initial results suggest that there is a negative relationship between oil price and

all the exchange rates. A one percent increase in the oil price result in a .269 percent

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Variable Observations: Mean: Std. Dev: Minimum: Maximum:

NOK/GBP 186 11.02444 1.44675 8.607 13.6376NOK/SEK 186 87.60742 4.436414 79.11 99.49NOK/USD 186 6.696975 1.12064 5.0546 9.3613Oil Price 186 63.5071 29.44867 19.39 133.88


decrease in NOK/USD exchange rate which by economic theory explains an appreciation of

the Norwegian krone against the US dollar. All variables were statistically significant at all

relevant levels except the output variable for NOK/USD and NOK/GBP exchange rate.

All variables except the money supply differential for USD and SEK exhibit the signs

expected by economic theory. The absolute monetary model presented earlier in this paper

explains that when a country´s money supply increases relative to a foreign country one

should expect a depreciation of the home country currency in the long run. My initial results

do not support this theory; one explanation could be that the PPP doesn’t hold because of

barriers to trade such as transaction cost etc. The R2 explains how much of the variation in

the exchange rate are covered by the variables in the regression. A R-squared(R2) of 0.8226

tells us that 82.26% of the variation in the NOK/USD exchange rate is a result of the

independent variables in my model, an acceptable level in econometric analysis.

3.4 – Testing for Unit Root, Cointegration, Heteroskedasticity & Serial Correlation

Since I am working with time series data there are some tests that have to be

conducted as time series data tend to exhibit time trends. If variables trend together it can

reveal underlying relationships, and the results will end up being biased. By performing a

few tests that are common for time series data, these trends can be removed. For the sake

of the length of this paper will only test results from the NOK/USD result be discussed, the

same tests for the other exchange rates can be found in the appendix. The following section

explains why the tests are important, how to conduct them and the related results.

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Test for stationary variables:A time series is stationary if its mean and variance are constant over time. The main

reason why it is important to know whether a time series is stationary or non-stationary

before regression analysis is that there is danger of obtaining apparently significant

regression results from unrelated data when non-stationary time series are used in

regression analysis. Such regressions are said to be spurious. One of the most commonly

used methods is the Augmented Dickey-Fuller (ADF) test for unit root. Where the H0= unit

root. If I fail to reject the H0 hypothesis the variable will be stationary, called an AR (1)

variable. The null hypothesis (H0) will be rejected if the test statistic is greater (in

absolute value) than the critical values related to the 1,5 and 10% level of significance. The

following table presents the results that where generated from the ADF-test on the

NOK/USD relationship:

The results from the Dickey-Fuller (ADF) test tells me that I fail to reject the null

hypothesis for all the variables in this regression since the test statistics are smaller (in

Variable Test statistic1% Critical

value5% critical

value10% critical

valueLog NOK/USD

FX -8.656 -2.589 -1.950 -1.616

Log MS (NOR-US) -14.500 -2.589 -1.950 -1.616

Log IP (NOR-US) -18.625 -2.589 -1.950 -1.616Log Oil Price -8.656 -2.589 -1.950 -1.616

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Variable Test statistic 1% critical value 5% critical value 10% critical valueLog NOK/USD FX -1.190 -3.482 -2.884 -2.574

Log MS (NOR-US) -0.931 -3.482 -2.884 -2.574

Log IP (NOR-US) -1.610 -3.482 -2.884 -2.574log Oil Price -1.868 -3.482 -2.884 -2.574


absolute value) than the critical values. This is true for all the variables. The following

table presents the ADF test results for the first differenced variables:

When taking the first difference of the variables, the result commonly rejects the null

hypothesis of unit root since the test statistic is greater than the critical values. This result

tells me that all variables are stationary in first differenced form. Economic theory calls these

kind of variables integrated of order one, or I (1).

Since the variables only become stationary when first differenced, tests for

cointegration will be necessary. With cointegration present in the regression it will cause a

spurious regression. Cointegration could be explained simply by using the example of a

drunk man and a dog. If the dog walks away from its owner, the owner tends to follow the

same direction. So cointegration explains how two variables are moving in the same

direction, resulting (as mentioned) in a spurious regression that will not be any good for

forecasting or analyzing. If the first differenced variables don’t show signs of cointegration it

will still be possible to analyze the regression without being afraid of biased results.

A test that is commonly used for cointegration testing is the Engel-Granger test. The

first step of this test is to run the basic regression and then generate a separate variable for

the error term followed by a ADF test on the error term. The results of the tests are listed

below.

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1. The first step is to perform the original regression.

2. Then I generated the residual from the regression and performed the Engel-Granger test on the residuals to test for

cointegration in the regression

Here we look at the p-value for the lagged residual variable L1. When the p-value

approach is used we will be able to reject the H0= cointegration if the p-value is smaller than

the 5% t-critical level. Here we see that the p-value is 0.001 which mean that I reject the null

hypothesis, and conclude that the residuals don’t show signs of cointegration. Since the

variables were stationary in first differenced form, and now turned out not to be

cointegrated I will be able to use the regression results for analytical purposes. In addition to

unit root and cointegration, I also need to check for heteroskedasticity and serial correlation.

When error terms from different time periods are correlated, we say that the error

term is serially correlated. Serial correlation occurs in time-series when the errors associated

with a given time period carry over into future time periods. For example, if we are

predicting exchange rate growth, an overestimate in one year is likely to lead to

overestimates in succeeding years. Serial correlation will not affect the unbiasedness or

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NOK/USD coefficient STD. Error T-Value P-value # Obs. R^2Adj. R^2

MS (NOR-US)

-0.1130645 0.396538 -2.85 0.005 184 0.8226 0.8197

IP (NOR-US)-

0.10447590.101979

3 -1.02 0.307

Oil Price-

0.2691326 0.249354 -10.79 0.000

Variable Coefficient Std. Error T-statistic P-ValueL1. -0.0959049 0.0290141 -3.31 0.001LD -0.1894631 0.0722732 2.62 0.009


consistency of regression estimators, but it does affect their efficiency. With positive serial

correlation, the estimators of standard errors will be smaller than the true errors. This will

lead to the conclusion that the parameter estimators are more precise than they really are.

The Breusch-Godfrey test is used to test for serial correlation in a time series

regression. When I tested the NOK/USD regression for serial correlation in the residuals the

following results were generated:

What I am looking for here is the P-value related to the Chi-squared distribution.

Here we have H0= no serial correlation, and with a probability of 0.000 I reject the null

hypothesis since the p-value is smaller than any critical value of interest (1%, 5%, 10%). By

rejecting the H0 I conclude that my time series regression does show signs of serial

correlation. Serial correlation can be corrected by using the Newey-West estimation method.

This method corrects for the serial correlation and allows us to correctly interpret the

regression results. Another feature of the Newey-West function is that it corrects for

heteroskedasticity. Heteroskedasticity is often not the main issue in time series regression

analysis, but it is important to be aware of what it means and how it is relevant. If a variable

involves heteroskedasticity it basically means that there are variations in the error terms

over the data set. In order for the F-test and T-test to be justified in a regression,

23

Breusch-Godfrey test for Serial CorrelationLags Chi-Squared DF Prob. > Chi-Squared

1 156,407 1 0.0000H0 = no serial correlation


heteroskedasticity can’t be present. So with the Newey-West results I do not have to be

concerned about heteroskedasticity.

The results generated from the Newey-West estimations for all three exchange rate

relationships are explained in the following table:

The results generated from the Newey-West estimation yields stronger evidence of

statistical significance than the initial regression discussed earlier in this paper. As we can

see from the table above, industrial production is still not statistically significant at any level

for both USD and GBP exchange rate, but the p-value has decreased from .307 to .234 for

USD, so the corrections I have done made it more fitted to the regression. The money supply

and oil price variables are still statistically significant at all levels, and therefore results from

these can be analyzed. The coefficient on the logarithmic oil price variable has the same

negative sign as in the initial model, and a 1% increase in the oil price will according to my

monetary approach regression correspond in a .269% appreciation of the Norwegian krone

against the U.S. Dollar. After conducting tests for the most common issues with time series, I

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Variable NOK/USD P-value NOK/SEK P-value NOK/GBP P-valueMoney Supply -0.1130645 0.000 -0.2466841 0.000 0.4521856 0.000Output -0.1144759 0.234 -0.205999 0.008 -0.1111683 0.616

Oil Price -0.2691326 0.000 -0.0265537 0.066 -0.1873946 0.000Constant

5.215184 0.000 4.421817 0.000 3.286315 0.000


am able to rely on the results generated above, and can conclude that the oil price has a

significantly negative impact on all the exchange rates used in my research.

4.1 Conclusion:

This paper was initially aimed at empirically testing how fluctuations in the oil price

impacts value of the Norwegian Krone, measured against important trading partner’s

currencies. Through econometric analysis I was able to generate a result that can be used for

analytical purposes. I succeeded in finding a statistically significant negative relationship

between the oil price and the Norwegian exchange rate, and the negative relationship is

supported by multiple economists discussed in the literature review section of this capstone

paper. The negative relationship suggested by my results highlights the importance of being

able to find alternative revenue streams in countries that heavily depends on a commodity,

as volatility always will be present. 25% of total value creation in Norway is generated in the

petroleum sector, and when knowing that oil price is a main driver behind fluctuations in the

value of Norwegian currency, policy makers in Norway are facing a new situation where the

focus should be aimed at preventing economic downswings caused by decreasing oil prices

in the future. The substantial cutbacks from the petroleum sector we witness today could be

the beginning of a new era for the Norwegian economy. One reason is that the oil-era one

day will be over, and secondly because the world economy jointly focuses more heavily on

renewable energy resources.

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Further research on the political aspect linked to the recent downswing in Norwegian

petroleum sector would be an interesting approach to compliment the research presented in

this capstone paper.

Work Referenced

Akram Farooq Q. (2004). “Oil prices and exchange rates: Norwegian example,” The Econometrics Journal, Vol.7, No. 2, 476-504

Akram, Qaisar. (2000) “When does the oil price affect the Norwegian exchange rate?”. Research Department, Norges Bank.

Blomberg, S. B. & Harris, E. S. (1995). “The commodity-consumer price connection: fact or fable”? Economic Policy Review. 1

Bjørk, Line & Mork, A. Knut & Uppstad, H. Bernt. (1998). “Påvirkes kursen på norske kroner av verdensprisen på råolje?”. Norsk Økonomisk Tidsskrift. Vol.1-33.

Bjørnland, H.C. (2009). “Oil price shocks and stock market booms in an oil exporting country”. Scottish Journal of Political Economy. 232-254

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Burbridge, J. & Harrison, A. (1984) “Testing for the effects of oil-price rises using vector autoregression. International Economic Review, Vol. 25, 459-484.

Gisser, M. & Goodwin, T.H. (1986). “Crude oil and the macroeconomy: Tests of some popular notions. Journal of Money, Credit, and Banking. Vol 18, 95-103.

Golub, S. S.”Oil prices and exchange rates”. The Economic Journal, (1983) 576-593.

Hamilton, J. D. (1983): Oil and the macroeconomy since World War II. Journal of Political Economy, 21, 228-248.

Hill, C. R & Griffiths, E. William & Lim, C. Guay. (2011). “Principles of Econometrics”. Fourth Edition. John Wiley & Sons Inc. (p.335-395)

Kalcheva, Katerina and Oomes, Nienke (2007), “Dutch Disease: Does Russia Have the Symptoms?” forthcoming BOFIT Discussion Paper 6/2007.

Kleivset. Christoffer. (2012). “From a fixed exchange rate regime to inflation targeting: A documentation paper on Norges Bank and monetary policy”. Norges Bank´s bicentenary project.

Kloster, Arne & Lokshall, Raymond & Røisland, Øistein. (2003). “To what extend can movements in the krone exchange rate be explained by the interest rate differential?”. Occasional paper. Norges Bank. Vol. 31.

Korhonen, Iikka & Juurikkala Tuuli. (2007). “Equilibrium exchange rates in oil-dependent countries”. BOFIT discussion papers.

Krugman, P. R. (1980). “Oil and the dollar”. National Bureau of Economic Research- Cambridge, Mass. USA.

Lizardo, R. A. & Mollick, A. V. (2010). “Oil price fluctuations and US dollar exchange rates”. Energy Economics, vol.32, 399-408.

Louganini, P. (1986) “Oil price shocks and the dispersion hypothesis”. Review of Economics and Statistics. Vol 58, 536-539.

Meese, R. & Rogoff, K. (1988) “Was it Real? The exchange rate-interest differential relation over the modern floating-rate period.” The Journal of Finance, vol.43, 933-948.

Nordbø, W. Einar & Stensland, Njål. (2015) “Oljevirksomheten og Norsk økonomi”, Working paper nr 4, Norges Bank.

Rapach, E. David & Wohar, E. Mark. (2002) “Testing the monetary model of exchange rate determination: New evidence for a century of data”. Journal of International Economics. Vol 58.

Steigum, Erling. (2010). “Norsk Økonomi etter 1980: Fra Krise til Suksess”, Working Paper Series 4/10.

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Zalduendo, Juan (2006), “Determinants of Venezuela’s equilibrium real exchange rate”, IMF Working Paper 06/74, Washington D.C.

Data retrieved from: https://research.stlouisfed.org

Appendix:

Test for Serial Correlation NOK/SEK:

Breusch-Godfrey LM test for

autocorrelationlags(p) chi2 df Prob > chi2

1 161.421 1 0.0000H0: no serial correlation

Test for Serial Correlation NOK/GBP:

Breusch-Godfrey LM test for

autocorrelationlags(p) chi2 df Prob > chi2

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https://research.stlouisfed.org/


1 166.433 1 0.0000H0: no serial correlation

Both exchange rates exhibit signs of serial correlation as we reject the H0 of no serial correlation in both cases. Hence further action, as discussed in the results section of this paper, will be necessary in order to generate unbiased results that can be trusted and analyzed.

Test for stationarity – (NOK/SEK) & (NOK/GBP)

Variable Test Statistic1% Critical Value 5% Critical Value 10% Critical Value

log NOK/SEK FX -2.071 -3.482 -2.884 -2.574Log MS NOK/SEK -1.382 -3.482 -2.884 -2.574Log IP NOK/SEK -3.012 -3.482 -2.884 -2.574Log Oil Price SWE -1.868 -3.482 -2.884 -2.574Log NOK/GBP FX -1.235 -3.482 -2.884 -2.574Log MS NOK/GBP -1.874 -3.482 -2.884 -2.574Log IP NOK/GBP -5.191 -3.482 -2.884 -2.574Log Oil Price UK -1.868 -3.482 -2.884 -2.574

When testing for stationarity only output variable in both exchange rates are significant (greater than any critical value), so need to take first difference and test again.

Test for stationarity on First Differenced Variables:

Variable Test Statistic1% Critical Value 5% Critical Value

10% Critical Value

log NOK/SEK FX -10.755 -2.589 -1.95 -1.616Log MS NOK/SEK -17.508 -2.589 -1.95 -1.616Log IP NOK/SEK -17.797 -2.589 -1.95 -1.616Log Oil Price SWE -9.713 -2.589 -1.95 -1.616Log NOK/GBP FX -11.038 -2.589 -1.95 -1.616Log MS NOK/GBP -16.636 -2.589 -1.95 -1.616Log IP NOK/GBP -18.896 -2.589 -1.95 -1.616

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Log Oil Price UK -9.713 -2.589 -1.95 -1.616

When first differenced, all variables exhibit test statistics greater than the respective 1% critical values. This means that all variables are AR (1). So both exchange rates will be suited for running the Newey West estimation as presented in the results section of this paper.

Additional Descriptive statistics

Variable Obs Mean Std. Dev. Min MaxMoney Supply

(NOR-US) 186 19.74673 .1797134 19.48142 20.1181

Money Supply (NOR-UK) 186 -.3031217 .0685066 -.4296932 -.1399994

Money Supply (NOR-SWE) 186 -.6875366 .0792685 -.8112049 -.5208645

Output (NOR-US) 186 .0991488 .1114652 -.1401052 .3063626

Output (NOR-UK) 186 .0311902 .0439245 -.1039081 .1381326

Output (NOR-SWE) 186 .0627297 .09581 -.1467066 .2557259

log NOK/GBP 186 2.391394 .133058 2.152576 2.612831log NOK/SEK 186 4.471603 .0502665 4.370839 4.600057log NOK(USD 186 1.888734 .1583778 1.620299 2.236584log Oil Price 186 4.025661 .5251023 2.964757 4.896944

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Documents

Capstone Paper - Final version