The Role of Monetary Shocks and Real Shocks on the Current Account, Terms of Trade and Real Exchange Rate Dynamics:

a SVAR analysis

Yanchun Zhang*# San Francisco State University

Abstract The paper is an empirical investigation of the role of monetary shocks and real shocks on the current account, terms of trade, and real exchange rate dynamics. Three new open economy macroeconomics (NOEM) models are studied to motivate the empirical investigation. Then I apply the Blanchard-Quah decomposition to identify a structural vector auto-regression model. The empirical evidence supports the NOEM model with the simplified household preference specification and with the small degree of pricing-to-market. The evidence also illustrates monetary shocks important for the real exchange rate and the terms of trade movements but not for the current account fluctuations. Keywords: Current Account; Terms of Trade; Real Exchange Rate;

Structural Vector Autoregression; Blanchard-Quah approach JEL classification: F31, F32, F41 * This paper is revised from the second chapter of my PhD thesis completed at University of Virginia. I thank my dissertation advisors Dr. Eric van Wincoop, Chris Otrok, and Jinill Kim for helpful discussions. All remaining errors are the author’s. The funding from Bankard Foundation and FERIS Foundation of America is appreciated. # Contact address: Yanchun Zhang, Department of Economics, San Francisco State University, 1600 Holloway Ave., San Francisco, CA 94132. Email address: yanchun@sfsu.edu. Tel: (415) 338-7516. Fax: (415) 338-1057.

1 Introduction

New Open Economy Macroeconomics (NOEM) models have been used by many econo-

mists today to study important issues in international macroeconomics such as the current

account dynamics and the real exchange rate uctuations. But there are quite a few open

debates that remain in the literature. Among them, one is the speci�cation of household

preferences, and another is whether or not pricing-to-market is a more realistic assumption

than pricing-in-the-producer's-currency.

In this paper, �rst I study three NOEM models and �nd the model solutions are di�erent

depending on the assumptions made about the parameter values in the household preference

function and about the degree of pricing-to-market. Then, I will apply a structural vector

autoregression (SVAR) model to evaluate the empirical implications of three NOEM models.

The empirical investigation helps to answer which model is preferable and how realistic

assumptions that the simpli�ed household preference structure and pricing-to-market are.

The �rst baseline NOEM model has a general household preference speci�cation. Utility

is modeled as a constant-rate-of-risk-aversion form in aggregate consumption and a real

money balance. Aggregate consumption is taken to be a constant-elasticity-of-substitution

index over the traded and nontraded goods consumption. The baseline model generates the

ambiguous results, and model solutions depend on the interplay of all the parameters in the

model. On the one hand, this \everything is possible" conclusion supports criticisms that

the model solutions are sensitive to the assumptions made about the household preference

structure. On the other hand, it suggests that empirical investigation is useful since the

theory does not clearly predict the economic responses to shocks.

The second model is a special case of the baseline model. Three parameters (the con-

sumption elasticity of the money demand, the intertemporal elasticity of substitution, and

the intratemporal elasticity of substitution) are all restricted to be 1. This special case is

worth considering given the analytical tractability. The model produces clear patterns of the

current account as well as the terms of trade and the real exchange rate dynamics responding

1

to the monetary and productivity shocks. If these predictions can �nd empirical support, the

implication will be that the simpli�ed preference structure may be enough to characterize

the economic reality.

The third model is a pricing-to-market model in which some �rms are able to price

di�erentiate the traded goods between international and domestic markets. The pricing-

to-market arrangement in combination with sticky prices allows the real exchange rate to

uctuate. Comparing the results from this pricing-to-market model and the special case

of the baseline model, I �nd many predictions about the responses of the current account,

the terms of trade, and the real exchange rate to exogenous shocks to be consistent in the

two models. But the pricing-to-market model also �nds some di�erent results with speci�c

degrees of pricing-to-market. For example, with full pricing-to-market, the short run current

account remained in balance. With a large degree of pricing-to-market, the terms of trade

were found to be improving instead of deteriorating even after a currency depreciation. These

di�erent predictions need to be checked via an empirical investigation.

To evaluate models on the basis of their performance, one way is to describe how the

economy responds to a macroeconomics shock. For this purpose, useful empirical evidence

is provided by the impulse response functions generated by vector autoregression (VAR)

econometric models.

The SVAR system I will investigate focuses on how three jointly determined endogenous

variables (the current account, the terms of trade, and the real exchange rate) respond to

three exogenous shocks: monetary shocks, productivity shocks in the traded goods sector,

and productivity shocks in the nontraded goods sector. I will apply a Blanchard-Quah

approach to obtain the structural identi�cation by imposing restrictions on the long run

behavior of three variables in the model. These long run restrictions are derived directly

from the underlying new open economy macroeconomics models.

After the SVAR model is identi�ed, empirical evidence is provided by the impulse re-

sponse functions and the forecast error variance decomposition results generated by the

SVAR model. Comparing the impulse responses generated by the SVAR model to the short

run and the steady state e�ects solved by the underlying new open economy macroeconomics

models, I �nd that the new open economy macroeconomics model with the simpli�ed house-

2

hold preference structure replicates the data well. Empirical evidence also shows that the

theoretical model is more consistent with the data when the degree of pricing-to-market is

between zero and a half. The forecast error variance decomposition results illustrate that

monetary shocks account for a substantial fraction of the terms of trade uctuations and

that they are also quantitatively important for the real exchange rate movements. There is

no signi�cant evidence showing that monetary shocks play an important role in the current

account uctuations except for Japan, Germany, and the US.

The remainder of the paper is structured as follows. Section 2 presents the underlying

new open economy macroeconomics models and their solutions. Section 3 discusses my

trivariate SVAR model and the empirical results. The last section is the conclusion.

2 The Underlying New Open EconomyMacroeconomics

Theory

In this section, I will present three two-country new open economy macroeconomics

models. Since the home country and the foreign country are symmetric, I will mainly focus

on equations for the home country.

2.1 The Baseline Model

First I look at a two-country, two-sector open economy model with sticky prices which

is structured similar to Obstfeld and Rogo� (1995). The maximization problems of home

country and foreign country representative consumer-producers are shown here.

Ut =1Xs=t

�s�t[�

� � 1C��1�

s +�

1� "(Ms

Ps)1�" � �T;s

2yT;s

2 � �N;s2yN;s

2];

U�t =1Xs=t

�s�t[�

� � 1C���1

�s +

�

1� "(M�s

P �s)1�" �

��T;s2y�T;s

2 ���N;s2y�N;s

2];

The utility function exhibits \constant relative risk aversion" (CRRA): The aggregate

consumption Ct itself is constructed as a \constant elasticity of substitution" (CES) index

3

of the traded and nontraded goods consumptions.

Ct = [ 1�C

��1�

T;t + (1� )1�C

��1�

N;t ]�

��1 ;

The home country traded and nontraded goods consumption variables make up the CES

real consumption index

CT;t = [Z 1

0cT;t(z)

��1� dz]

���1 ;

CN;t = [1

n

Z n

0cN;t(z)

��1� dz]

���1 ;

where cT;t(z) is the home country individual's consumption of the traded goods z; cN;t(z)

is the home country individual's consumption of the nontraded goods z, and � is the price

elasticity of the demand.

In this economy, it is assumed that the only internationally traded asset is a riskless real

bond denominated in the composite traded consumption goods CT;t. The period budget

constraint for a representative home country individual can be written in nominal terms as

PT;tBt+1 +Mt = PT;t(1 + rt)Bt +Mt�1 + pT;tyT;t + pN;tyN;t � PT;tCT;t � PN;tCN;t � PT;t�t:

Here rt denotes the real interest rate on bonds between period t-1 and t, yT;t is the output

of home-produced traded goods, and pT;t is its domestic-currency price. yN;t and pN;t are the

corresponding nontraded goods output and price levels, Mt�1 is the individual's holdings of

nominal money balances entering period t, and �t denotes lump-sum taxes payable in CT;t.

It is assumed that the government balances its budget for each period.

The law of one price is assumed to hold for the traded goods. The domestic-currency

price for domestic goods, p, and the foreign country-currency price for foreign country goods,

p�, are set one period in advance, but they adjust to exible-price levels after one period if

there are no new shocks. The short run output is assumed to be demand-determined.

In this economy, the short run is period 1, and the steady state is from period 2 on.

The variable without a time subscript is de�ned as the steady-state variable. The study will

4

focus on the short run and the steady-state e�ects of three exogenous shocks on the current

account, the terms of trade, and the real exchange rate. Three exogenous shocks in this

open economy are relative monetary shocks (M1� M�1 ),

1 relative productivity shocks to the

traded goods sector �(�T;1� ��T;1),2 and relative productivity shocks to the nontraded goodssector �(�N;1 � ��N;1). All shocks in the economy are assumed to be permanent.

The short run and long run e�ects of three exogenous shocks are summarized as

CA1 = c1(M1 � M�1 ) + c2(�T;1 � ��T;1) + c3(�N;1 � ��N;1);

CA = 0;

TOT1 = c4(M1 � M�1 ) + c5(�T;1 � ��T;1) + c6(�N;1 � ��N;1);

TOT = c7(M1 � M�1 ) + c8(�T;1 � ��T;1) + c9(�N;1 � ��N;1);

RER1 = (1� )[c4(M1 � M�1 ) + c5(�T;1 � ��T;1) + c6(�N;1 � ��N;1)];

RER = c10(M1 � M�1 ) + c11(�T;1 � ��T;1) + c12(�N;1 � ��N;1):

where c1| c12 are functions of parameters �, �, �, ", �, and n. � is the intertemporal

elasticity of substitution; � is the intratemporal elasticity of substitution; n is the relative

size of the home country set apart from the whole world; � is the price elasticity of demand; 1"

is the consumption elasticity of the money demand; � describes the rate of time preference,

and is the proportion of the traded goods consumption in the aggregate consumption.

These parameters are all non-negative. � > 1 is required to ensure the positive output of a

monopolist; n and � are between 0 and 1. The signs of coe�cients c1| c12 depend on the

interactions of all the parameters in the model.

1De�ne X1 to be the percentage deviation of the period 1 variable from the initial steady state in period0, i.e. X1 = (X1� �X0)= �X0 ; de�ne X to be the percentage deviation of the long-run variable from the initialsteady state, i.e. X = ( �X � �X0)= �X0:

2A negative �T;1 implies a positive productivity shock to the tradable sector.

5

2.2 A Special Case Where � = � = " = 1

In this section, I will look at a special case of the baseline model. I will take the values

of parameters �; � and " to be 13 as in Obstfeld and Rogo� (1995). The purpose is to get

the tractable model solutions.

When � = � = " = 1; the maximization problem of the home country representative

consumer-producer becomes

Ut =1Xs=t

�s�t[ logCT;s + (1� ) logCN;s + � logMs

Ps� �T;s

2yT;s

2 � �N;s2yN;s

2]:

The short run real exchange rate, the current account, and the terms of trade are solved

as

RER1 = �(1� )[�(1 + �) + 2�

��(1 + �) + 2�(M1 � M�

1 ) +(�� 1)

��(1 + �) + 2�(�T;1 � ��T;1)];

CA1 =2(1� n)(�� 1)�(1 + �) + 2

(M1 � M�1 ) +

(1� n)(�� 1)�(1 + �) + 2

(�T;1 � ��T;1);

TOT1 � ��(1 + �) + 2�

��(1 + �) + 2�(M1 � M�

1 )�(�� 1)

��(1 + �) + 2�(�T;1 � ��T;1);

where � = 1���. The current account goes back to zero from period 2 on. The long run

terms of trade and the real exchange rate are solved as

TOT =�(�� 1)

��(1 + �) + 2�(M1 � M�

1 ) +��+ 1

��(1 + �) + 2�(�T;1 � ��T;1);

RER =(1� )�(�2 � 1)��(1 + �) + 2�

(M1� M�1 )+ (1� )[

1

2(�N;1� ��N;1)�

(�� 1)��(1 + �) + 2�

(�T;1� ��T;1)]:

The model generates tractable solutions. One interesting result found here is that pro-

ductivity shocks to the nontraded goods sector do not enter the long run terms of trade

3Mankiw and Summers (1986) �nd an estimation of the consumption elasticity of money demand to bea number close to one. Betts and Devereux (1996), Hau (2000) also suggest a unit money demand elasticityin their papers.

6

expression. This result is di�erent from the generalized model discussed before. When

� 6= �; the traded goods consumption is a�ected by the nontraded goods consumption. Thespillover e�ect between the traded goods consumption and the nontraded goods consump-

tion depends on the relative strength of � and �. When � = �; there is no spillover e�ect

between the traded goods consumption and the nontraded goods consumption. Thus, the

productivity growth in the nontraded goods sector has no e�ect on the terms of trade.

2.3 A Pricing-to-Market Model

I will follow Betts and Devereux (2000) and assume that a fraction of s �rms in each

country are able to di�erentiate between the domestic market and the foreign country market

and to charge di�erent prices in these two markets. For these s proportion of �rms, goods

prices are set in the consumers' currency.

The home country representative consumer-producer maximizes the utility function.

Ut =1Xs=t

�s�t[�

� � 1C��1�

s +�

1� "(Ms

Ps)1�" � �s

2ys2]

All goods are international traded goods. The domestic-currency price index for overall

real consumption C takes a new form as

Pt = [Z n

0pt(z)

1��dz +Z n+(1�n)�s

np�t (z)

1��dz +Z 1

n+(1�n)�setq

�t (z)

1��dz]1

1��

The home country price index Pt includes three parts: pt(z) represents the home country

currency prices of the home country-produced goods; p�t (z) is the home country currency

prices of foreign country pricing-to-market goods z while q�t (z) is the foreign country currency

price of foreign country non-pricing-to-market goods.

The period budget constraint for a representative home country individual j becomes

Bt+1=(1 + it+1) +Mt = Bt +Mt�1 + ptyt � PtCt � PT;t�t:

Here, Bt+1 is the holdings of nominal bonds denominated in home country currency4.

4It is necessary to de�ne Bt+1 as a nominal indexed bond in this pricing-to-market model instead of areal indexed bond in the baseline model because with the pricing-to-market feature, the traded goods pricesare no longer equated across countries.

7

it+1 is the nominal interest rate on bonds between period t and t+1.

As in the previous sections, prices are set in advance and cannot be adjusted for shocks

in period 1. For pricing-to-market �rms, nominal prices are set in the consumer's currency.

For other �rms, nominal prices will be set in the producer's currency.

I will solve the short run exchange rate changes as

e1 =(1 + 1

"�)(M1 � M�

1 ) +1"(1 + 1

"�)

(��1)(�+1)

�+�+��1�+1

(�1 � ��1)

(1� s) + ("�s)"2�

+ 1"(1 + 1

"�)[ (1�s)(��1)1+ 1

�(�+��1

�+1)+ s]

:

The short run current account, terms of trade, and real exchange rate are solved as

CA1 = (1� n)(1� s)(�� 1)(� + ��1

�+1)

(� + � + ��1�+1)e1 + (1� n)

� (��1)(�+1)

� + � + ��1�+1

(�1 � ��1):

TOT1 = (2s� 1)e1:

RER1 = �se1:

In the long run, all prices are fully anticipated, and the degree of pricing-to-market is

irrelevant. Given the symmetry of the model, CA = 0 must hold; due to the equal markups

across countries assumed in the model, the terms of trade must equal unity; there is a full

purchasing power parity, and the real exchange rate goes back to the initial steady state

level.

2.4 The Summary of Three Theoretical Models

The results from the three models are summarized in Table 1. Three endogenous variables

are CA (the current account), TOT (the terms of trade), and RER (the real exchange

rate). In the tables, P, M, and NP represent productivity shocks in the traded goods sector,

monetary shocks, and productivity shocks in the nontraded goods sector. The + and - signs

demonstrate whether or not three endogenous variables will increase or decrease after the

8

occurrence of one unit of positive exogenous shocks assuming everything else holds constant.

The 0 means there is no change in the endogenous variables after the shocks.

The �rst generalized model produces theoretical ambiguity concerning the direction of

the current account, the terms of trade, and the real exchange rate movements. The question

marks in the table show that we can not conclude which direction the current account, the

terms of trade, and the real exchange rate are moving to after the shocks. They imply

that the Obstfeld and Rogo� (1995) model solutions are sensitive to the parameter values.

Therefore, an empirical investigation is needed since the theory fails to give a clear prediction

with uniform results.

The second model is a special case of the baseline model. The preference structure is

simpli�ed because the traded goods consumption, the nontraded goods consumption, and

the real money balance enter the utility function separately in log form. The model �nds

that in the short run, a relative positive monetary shock generates a real exchange rate

depreciation, a terms of trade deterioration, and a current account surplus; a productivity

growth in the traded goods sector generates a real exchange rate appreciation, a terms of

trade improvement, and a current account de�cit; a productivity growth in the nontraded

goods sector has no e�ect. In the long run, the current account goes back to a balanced

state; a monetary expansion improves the terms of trade and appreciates the home currency;

a productivity growth in the traded goods sector appreciates the home currency but deterio-

rates the terms of trade; a productivity growth in the nontraded goods sector a�ects the real

exchange rate and tends to depreciate the home currency in the long run. Whether or not

this simpli�ed household preference structure is su�ciently accurate as a characterization of

what really is needs to be empirically investigated.

The third pricing-to-market model �nds that in the short run, a money increase generally

depreciates the home currency and leads to a current account surplus. With a zero pass-

through, the real exchange rate does not respond to the monetary shock. On the other hand,

with a full pass-through, the current account stays in balance. A productivity growth in the

traded goods sector is found to appreciate the home currency and leads to a current account

de�cit. The only exception is that under a zero pass-through, the real exchange rate remains

constant.

9

The direction of a terms of trade movement responding to a money increase depends

crucially on the degree of pricing-to-market. When more than half of the �rms are able to

di�erentiate markets perfectly and set di�erent prices in the home and foreign markets, the

exchange rate uctuation will not pass through to the local currency price of the goods, and

the terms of trade will improve because the depreciation raises the home currency price of

exports but leaves import prices unchanged. When there is less than half of the �rms able

to price-to-market, the terms of trade must deteriorate. When half of the �rms price to

market and the other half price traded goods in the producer's currency, the rise in export

prices cancels out the rise in import prices, and the terms of trade remain unchanged. These

conclusions also need to be checked via empirical investigations.

3 Empirical Implementation

3.1 Estimating the SVAR

The structural model is written as

A0yt = k + A1yt�1 + A2yt�2 + A3yt�3 + :::+ Apyt�p + "t; (1)

where

yt =

264 CAtTOTtRERt

375 ; "t =

264 "Rtt

"Nt"Rnt

375 :"t is a vector of independent white-noise structural shocks, with each shock having a

constant variance normalized to 1. Three structural shocks are real shocks to the traded

goods sector "Rt, nominal shocks "N , and real shocks to the nontraded goods sector "Rn.

Instead of a bivariate SVAR model, I have used a trivariate model for three reasons.

First, the real exchange rate and the terms of trade are two relative prices closely related

to the current account. In the presence of feedback, it is more appropriate to use a VAR

system to treat all of the variables as being jointly endogenous. Second, a trivariate model

allows me to distinguish between real shocks in the traded goods sector and real shocks in

the nontraded goods sector. Such a distinction is important because these two shocks a�ect

10

the economy di�erently.5 For example, a relative positive productivity shock in the traded

goods sector will appreciate the real exchange rate, but a relative positive productivity shock

in the nontradable sector will lead to a domestic currency depreciation. Third, the pricing

to market model in section 2.2 has a di�erent implication concerning the terms of trade

movement after a country's currency depreciation. When more than half of the �rms choose

to price to consumers' market, the terms of trade will improve after a domestic currency

depreciation. This conclusion is contrary to the customary presumption. Finding whether

the terms of trade and the real exchange rate move in the same or opposite direction will

help to answer the reasonable degree of pricing to market that is consistent with the data.

(1) can also be compactly written as a structural vector moving average|SVMA(1),

yt = C(L)"t = C0"t + C1"t�1 + C2"t�2 + C3"t�3 + :::; (2)

The coe�cients of Ci are the impulse response functions, and the accumulated e�ects of

unit impulses in "t after n periods, C1, can be obtained following the appropriate summation

of the coe�cients of the impulse response functions.

In practice, I �rst estimate the reduced-form SVAR model

yt = B0 +B1yt�1 +B2yt�2 +B3yt�3 + :::+Bpyt�p + ut; (3)

3.2 Identi�cation of the SVAR Model

To identify the structural model from (3), it is necessary to impose 3 restrictions on

the structural model. Economists usually apply either the Sims-Bernanke approach or the

Blanchard-Quah approach to obtain a structural identi�cation. Sims (1986) and Bernanke

(1986) impose economically plausible restrictions on contemporaneous interactions among

the variables. On the other hand, Blanchard and Quah (1989) suggest imposing the long run

5Iscan (2000) �nds that the country-speci�c productivity growth in tradables and nontradables has dif-ferent implications for the current account dynamics.

11

restrictions on the behavior of the variables in the system to identify the SVAR system. Many

economists �nd long run restrictions plausible a priori. Ideally, these long run restrictions

are derived from the underlying intertemporal economic theory.

In this paper, I will use the long run restrictions implied in the underlying intertemporal

model with both traded and nontraded goods studied in section 2:2 to identify the structural

matrices Ci from the data.

This underlying standard new open economy macroeconomics model provides four zero

restrictions for the trivariate SVAR system. Each of the three exogenous shocks has no long

run e�ect on the current account. The fourth zero long run restriction derived from the

model shows that the productivity growth in the nontraded goods sector has no e�ect on

the terms of trade. These restrictions hold true for many open economy macroeconomics

models that have been noted in other sources as well.

The Blanchard and Quah approach only requires choosing three elements of the long

run moving average coe�cient matrix C1 to identify the system. Thus, this SVAR model is

over-identi�ed. I checked all the possible combinations for long run identi�cation restrictions

and noticed that if three zeros were put in the �rst row, it was impossible to distinguish three

exogenous shocks from one another. Thus, there were three combinations of identi�cation

restrictions left for consideration.

First, I looked at a SVAR model which is triangular in the long run.

C1=

264 C11 0 0C21 C22 0C31 C32 C33

375.That is the real shocks to the nontraded goods sector f"Rng have no long run e�ect on

the CA or TOT which implies that C13 and C23 equal zero; the restriction C12 = 0 is justi�ed

by the fact that the nominal shocks f"Ng have no e�ect on the level of the CA in the longrun.

The second long run moving average matrix C1 looks like this.

C1 =

264 0 C12 0C21 C22 0C31 C32 C33

37512

The third possible long run moving average coe�cient matrix assumes C11 and C12 to be

zeros.

C1 =

264 0 0 C13C21 C22 0C31 C32 C33

375Notice that here, the C1 � (C1)0 is symmetric with two zeros.

264 (C13)2 0 C13C33

0 (C21)2 + (C22)

2 C21C31 + C22C32C13 � C33 C21C31 + C22C32 (C31)

2 + (C32)2 + (C33)

2

375With �ve equations but six unknowns, it is impossible to solve C1 from C1 � (C1)0.

In the paper, I will use the �rst two combinations of long run restrictions to identify

the SVAR system. Empirical evidence from these two long run moving average coe�cient

matrices are then to be compared. If the impulse response functions and the forecast error

variance decompositions are robust to the selection of C1, they present a reasonably clear

pattern in the data.

3.3 Data

I have examined seven countries: Canada, France, Germany, Italy, Japan, the UK, and

the US. The dollar-denominated CA numbers were from the International Financial Statistics

(IFS) for the International Monetary Fund and were converted to national currencies by

using the bilateral exchange rate. The TOT data, de�ned as the price index of exports

divided by the price index of imports, was constructed from the Main Economics Indicator

database of the Organization of Economic Cooperation & Development. The price index

was approximated by using the implicit price de ator which was the ratio of the value of

current-price imports or exports over that of the base-year-price imports or exports. The

real e�ective exchange rate RER series was also taken from the IFS dataset. A real e�ective

exchange rate index was de�ned as a nominal e�ective exchange rate index adjusted for

relative movements in the national price or cost indicators of the home country and selected

countries including the Euro area. An increase in the index re ected an appreciation. The

13

real e�ective exchange rate data used in the paper was based on prices and export unit

values (WPI). Both of the series TOT and RER were converted to the natural logarithm.

The quarterly data was taken from 1975Q1 to 1999Q2. All of the data was seasonally

adjusted.

In order to use the Blanchard and Quah technique, all of the variables in yt had to be

in a stationary form. Unit root test results are reported in Table 2. I conducted both

the Augmented Dickey-Fuller (ADF) test and the Kwiatkowski et al (KPSS) test. The lag

length was either based on the Akaike Information Criterion (AIC) or the Bayes Information

Criterion (BIC).

For the CA and RER series, Table 2 indicates that the KPSS test rejects the trend-

stationary null at 5%, and the ADF test fails to reject the unit root null at 5%. It is true for

all seven countries, and it is also true for the test with both the AIC-based lag length and the

BIC-based lag length. For the TOT series, the KPSS tests with the lag length determined

by the AIC and the BIC reject the trend-stationary null for all of the countries except for

France. The ADF test rejects the unit root null for France and the UK which is also true

for the test with the lag length determined by both the AIC and the BIC. In this paper, I

found all three variables to be nonstationary.

The Johansen cointegration tests found a zero cointegration relationship among these

three endogenous variables for all countries. The trivariate SVAR model was thus estimated

in �rst di�erences. The model was estimated using 5 lags.

3.4 Impulse Response Functions

First, I presented the impulse response functions which described the propagation mech-

anism of the shocks. The results from two SVAR models are summarized in Table 3. The

impulse response functions provided a pretty clear picture of the current account, the terms

of trade, and the real exchange rate dynamics responding to the exogenous shocks.

The impulse response functions generated from the �rst SVAR model are displayed in

Figures 1-3. Solid lines are the point estimates of the impulse responses for the levels of CA;

TOT and RER to three exogenous shocks. Dashed lines represent the two standard deviation

14

con�dence intervals of the impulse responses from 1000 bootstrapping6 simulations of the

model. The narrowness of these bands indicates the statistical signi�cance of the estimated

impulse responses. Figures 1-3 show that this data gives a much clearer picture about the

short run and long run movements of CA; TOT , and RER responding to monetary shocks

and productivity shocks.

In the short run, responding to a relative home money increase, all currencies depreciate,

the terms of trade worsen for all countries, and the current account goes to a de�cit except

for Germany and the UK; Japan has an impact current account de�cit followed by a move to

a surplus. These movements are similar to what were concluded in the NOEM model with

the simpli�ed household preference as seen in section 2.2.

The direction of terms of trade and real exchange rate movements after a money increase

is consistent with the customary presumption and supports the NOEM model with the

traditional assumption about price stickiness in the producer's currency. Bergin (2003) �nds

that pricing-in-the-producer's-currency model performs uniformly less well than the pricing-

to-market model. The reason may be that Bergin (2003) does not include the terms of trade

variable in his empirical model. As Obstfeld and Rogo� (2000) point out, the traditional

assumption of pricing-in-the-producer's-currency explains the observed correlation between

the terms of trade and the real exchange rate better compared to the pricing-to-market

model. In fact, the positive correlation between the terms of trade and the real exchange

rate movements found in my SVAR model is consistent with the pricing-to-market NOEM

model only when the degree of pricing-to-market is between zero and a half.

The productivity growth in the traded goods sector appreciates the real exchange rate,

improves the terms of trade, and leads to a current account de�cit for all countries. The pro-

6The idea behind it is that we get the small-sample distribution of impulse response functions withoutassuming that the errors in the reduced-form regression are Gaussian distributed. We �rst estimated theSVAR and saved the coe�cient estimates and the �tted residuals futg : These �tted residuals would beresampled with a replacement of

nu(1)t

o. Based on the resampled residuals, we generated the values of y

(1)t

y(1)t = c+B1yt�1+B2yt�2+B3yt�3+:::+Bpyt�p+u

(1)t

A SVAR would be �tted by OLS to the simulated y(1)t . From this estimate, the coe�cients of C

(1)s were

calculated. Next, I generated a second set of the �tted residualsnu(2)t

oand the simulated y

(2)t and C

(2)s :

A series of 1,000 such simulations were undertaken, and a 95% con�dence interval was inferred from therange that included 95% of the values for Cs:

15

ductivity growth in the nontraded goods sector depreciates the real exchange rate, improves

the terms of trade except for Italy and Canada, and leads to a current account de�cit for all

countries except for Japan.

In the long run, the productivity growth in the nontraded goods sector depreciates the

home currency. Monetary shocks deteriorate the terms of trade and depreciate most curren-

cies although the British pound appreciates, and the German mark reverts to a steady state.

Responding to the productivity growth in the home traded goods sector, each country's

terms of trade improve except for Italy and Japan; four countries' currencies appreciate;

the British pound depreciates, and the French franc and the Japanese yen do not respond

signi�cantly to this shock.

The �nding of a persistent depreciation after a money increase is consistent with Eichen-

baum and Evans (1995). The long run depreciation provides evidence for a signi�cant de-

viation from the purchasing power parity and the uncovered interest rate parity in favor of

US interest rates.

The impulse response functions generated from the second SVAR model are similar to

those in the �rst model. The only exception is that the second SVAR model �nds that a

monetary expansion leads to a current account surplus in the short run for all of the countries

while the �rst SVAR model �nds a current account de�cit for most countries.

3.5 The Forecast Error Variance Decomposition

To illustrate the quantitative contribution of various shocks to the CA; TOT;and RER

volatility, I presented the forecast error variance decomposition results. The results tell

us about the proportion of the movements in a sequence that are due to various shocks.

Discovering whether or not monetary shocks matter provides the empirical evidence for

whether or not focus on the monetary shocks in the recent international dynamic general

equilibrium literature is well-founded.

The forecast error variance decomposition results are reported in Tables 4-6. The stan-

dard errors in parentheses are computed from 1000 bootstrapping simulations. The condi-

tional variances of the levels of the CA, TOT , and RER at various horizons are split into the

16

variances that are due to three exogenous shocks. As can be seen from the results reported,

"Rt accounts for a substantial fraction of the conditional variance of the level of the CA at

all horizons for all six countries studied. Neither "N nor "Rn is important for the CA volatil-

ity. "N accounts for less than 10 percent of the CA uctuations for all countries except for

Japan and the US. In Table 4, we see nominal shocks "N are attributed to 52 percent of the

1-quarter variance in Japan's CA: But the role of nominal shocks drops quickly after four

quarters. Monetary shocks only account for 7.5 percent of Japan's CA over a period of 20

quarters. For the US, monetary shocks explain about 30 percent of one-quarter uctuations,

but this number drops to 2 percent after accounting for the 20-quarter variance. This result

is smaller than the �nding of Lane (1999) where monetary shocks accounted for about half

of the variation in the US current account at horizons up to 20 quarters.

However, Table 5 shows that nominal shocks account for most of the conditional variances

of the TOT for all seven countries in both the short and long runs. But for most countries,

the role of nominal shocks drops from around 90 percent to around 60 percent after �ve

years; on the other hand, the real shocks contribute more to the terms of trade movements.

Real shocks to the nontraded goods sector "Rn are found to be very important for the

uctuations of the RER in the short term but become less important as the horizon expands.

With regard to the remaining variances in forecasting the level of the RER that are not

attributed to "Rn, virtually all of the di�erences for France, Japan, and the UK are attributed

to nominal shocks "N . Nominal shocks account for 20 to 40 percent of the variations in the

RER. For Japan, with a time period of 20 quarters, "N still accounts for more than 30

percent of the variance of Japan's RER.

In the second SVAR model, the forecast error variance decomposition results are almost

identical to what I found above. Monetary shocks are found to play a more important role in

explaining the economic uctuations. For example, with the exception of Japan and the US,

monetary shocks are also found to be important in explaining the short run current account

variance in Germany. The contributions of monetary shocks in terms of trade dynamics

appear to be more signi�cant. Nominal shocks are also found to be quite important for the

RER movements in France, Japan, and the UK.

17

4 Conclusion

This paper conducted a theoretical and empirical investigation of the role of monetary

shocks and real shocks on the current account, terms of trade, and real exchange rate dynam-

ics. First, three new open economy macroeconomics models were studied. Model solutions

were found di�erent depending on the assumptions made about the household preference

speci�cation and pricing-to-market.

To evaluate the empirical implications of these three new open economy macroeconomics

models, I have developed a trivariate SVAR model for the current account, the terms of

trade, and the real exchange rate. The long run identi�cation restrictions applied to the

SVAR models were derived directly from the underlying theoretical model. Since the theory

implied more long run restrictions than those that were needed, I checked the robustness of

the empirical results for two di�erent combinations of long run identi�cation restrictions.

The impulse response functions provided a clear pattern. The two SVAR models gener-

ated similar impulse responses except for the short run current account responses to monetary

shocks. The �rst SVAR model found that a relative home money increase led to a current

account de�cit for most countries while the second SVAR model found the opposite. When

comparing the impulse response functions generated by the SVAR models to the short and

long run e�ects predicted by the three new open economy macroeconomics models, I found

that the empirical evidence favored the model with the simpli�ed preference structure and

the model with the degree of international market segmentation between zero and a half.

The correlation between the terms of trade and real exchange rate movements was explained

better using the NOEM model with the traditional assumption of price stickiness in the

producer's currency. Excluding the terms of trade from his empirical model might explain

why Bergin (2003) found the evidence favoring pricing-in-the-consumer's-currency instead.

The forecast error variance decomposition results from the SVAR model illustrated that

monetary shocks account for a substantial fraction of the terms of trade uctuations and are

also quantitatively important for the real exchange rate movements. There was no evidence

showing that monetary shocks play any signi�cant role in the current account uctuations

of major economies except for those that were seen in Japan, Germany, and the US.

18

References

[1] Bernanke, B.S., 1986. Alternative explorations of the money-income correlation.

Carneigie-Rochester Conference Series on Public Policy 25, 49-99.

[2] Betts, C., Devereux, M.B., 1996. The exchange rate in a model of pricing-to-market.

European Economic Review 40 (3-5), 1007-1021.

[3] Betts, C., Devereux, M.B., 2000. Exchange rate dynamics in a model of pricing-to-

market. Journal of International Economics 50 (1), 215-244.

[4] Blanchard, O.J., Quah, D., 1989. The dynamic e�ects of aggregate demand and supply

disturbances. American Economic Review 79 (4), 655-673.

[5] Eichenbaum, M., Evans, C.L., 1995. Some empirical evidence on the e�ects of shocks to

monetary policy on exchange rates. Quarterly Journal of Economics 110 (4), 975-1009.

[6] Hau, H., 2000. Exchange rate determination: the role of factor price rigidities and

nontraded goods. Journal of International Economics 50 (2), 421-447.

[7] Iscan, T., 2000. The terms of trade, productivity shocks, and the current account.

Journal of Monetary Economics 45 (1), 189-211.

[8] Obstfeld, M., Rogo�, K., 1995. Exchange rate dynamics redux. Journal of Political

Economy 103 (3), 625-660.

[9] Obstfeld, M., Rogo�, K., 2000. New directions for stochastic open economy models.

Journal of International Economics 50 (1), 117-153.

[10] Bergin, P., 2003. Putting the "new open economy macroeconomic" to a test. Journal of

International Economics 60 (1), 3-34.

[11] Lane, R.P., 1999. Monetary shocks and the current account. Trinity College Dublin and

CEPR working paper.

[12] Mankiw, N.G., Summers, L., 1986. Money demand and the e�ects of �scal policies.

Journal of Money, Credit, and Banking 18 (4), 415-429.

19

[13] Sims, C.A., 1986. Are forecasting models usable for policy analysis? Federal Reserve

Bank of Minneapolis Quarterly Review, Winter, 2-16.

20

P M NP P M NPCA ? ? ? 0 0 0

TOT ? ? ? ? ? ?RER ? ? ? ? ? ?

P M NP P M NPCA - + 0 0 0 0

TOT + - 0 + 0RER + - 0 + + -

P M P MCA - +/0(s=1) 0 0

TOT ? ? 0 0RER +/0(s=0) -/0(s=0) 0 0

2. The Obstfeld and Rogoff (1995) Model with Both the Traded and Nontraded Goods

Table 1Summarized Results for Three NOEM Models

3. The Pricing-to-Market Model

Short Run Long Run

Short Run Long Run

1. The Generalized Model with Both the Traded and Nontraded Goods

Test Series Canada France Germany Italy Japan UK US

ADF(AIC) CA -2.786 -2.236 -1.623 -2.305 -2.021 -2.283 -1.649TOT -2.467 -3.842 ** -2.095 -2.359 -2.357 -4.740 ** -2.443RER -2.046 -2.610 -2.338 -2.066 -3.339 -1.950 -2.280

ADF(BIC) CA -2.786 -2.236 -1.623 -2.305 -2.021 -2.283 -1.649TOT -2.467 -3.842 ** -2.095 -2.359 -2.357 -4.740 ** -2.443RER -2.046 -2.643 -2.338 -2.066 -3.339 -1.950 -2.280

KPSS(AIC) CA 0.324 ** 0.561 ** 0.617 ** 0.488 ** 0.419 ** 0.449 ** 0.461 **TOT 0.446 ** 0.085 0.297 ** 0.361 ** 0.416 ** 0.154 ** 0.258 **RER 0.307 ** 0.386 ** 0.813 ** 0.720 ** 0.383 ** 0.457 ** 0.582 **

KPSS(BIC) CA 0.324 ** 0.561 ** 0.617 ** 0.488 ** 0.419 ** 0.449 ** 0.461 **TOT 0.446 ** 0.085 0.297 ** 0.361 ** 0.416 ** 0.154 ** 0.258 **RER 0.307 ** 0.549 ** 0.813 ** 0.720 ** 0.383 ** 0.457 ** 0.582 **

Table 2Tests for Unit Roots

ADF denotes the Augmented Dickey-Fuller test statistics for the unit root null hypothesis versus the trent-stationary alternative. KPSS denotes the Kwiatkowski et al (1993) test of the null of trend-stationarity. ** indicates rejection of the null at 5%. The lag length was based on the Akaike Information Criterion (AIC) or the Bayes Information Criterion (BIC).

P M NP P M NPCA - - - - 0 0

TOT + - + + - 0RER + - - + - -

P M NP P M NPCA - + - 0 + 0

TOT + - + + - 0RER + - - + - -

SVAR Model 2

Short Run Long Run

Table 3Summarized Impulse Responses from Two SVAR Models

SVAR Model 1

Short Run Long Run

Plots depict the responses of the CA, TOT, and RER to a monetary shock (M). Solid lines are point estimates, and dashed lines represent the two standard deviation confidence interval of impulse responses from 1000 bootstrapping simulations.

Impulse Response Functions of CA, TOT, and RER to M (SVAR Model 1)Figure 1

France: CA to M

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

France: TOT to M

-2.5

-2

-1.5

-1

-0.5

01 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Canada: CA to M

-0.025-0.02-0.015-0.01-0.005

00.0050.010.0150.020.025

1 4 7 10 13 16 19 22 25 28 31 34 37 40

Canada: TOT to M

-2

-1.5

-1

-0.5

01 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Canada: RER to M

-1.6-1.4-1.2-1

-0.8-0.6-0.4-0.201 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Germany: CA to M

-0.01

0

0.01

0.02

0.030.04

0.05

0.06

0.07

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Germany: TOT to M

-7

-6

-5

-4

-3

-2

-1

01 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Germany: RER to M

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Italy: CA to M

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Italy: RER to M

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

01 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Italy: TOT to M

-6

-5

-4

-3

-2

-1

01 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Japan: TOT to M

-5

-4

-3

-2

-1

01 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Japan: RER to M

-5

-4

-3

-2

-1

01 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

UK: CA to M

-0.004-0.002

00.0020.0040.0060.0080.010.012

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

UK: TOT to M

-8-7-6-5-4-3-2-101 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

UK: RER to M

-1

-0.5

0

0.5

1

1.5

2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Japan: CA to M

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

US: CA to M

-0.06-0.05-0.04-0.03-0.02-0.01

00.010.02

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

US: TOT to M

-2.5

-2

-1.5

-1

-0.5

01 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

France: RER to M

-1.2

-1

-0.8

-0.6

-0.4

-0.2

01 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

US: RER to M

-2

-1.5

-1

-0.5

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Figure 2Impulse Response Functions of CA, TOT, and RER to P (SVAR Model 1)

Plots depict the response of the CA, TOT, and RER to a productivity shock in the traded goods sector (P). Solid lines are point estimates, and dashed lines represent the two standard deviation confidence interval of impulse responses from 1000 bootstrapping simulations.

Canada: CA to P

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Canada: TOT to P

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Canada: RER to P

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

France: CA to P

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

France: RER to P

-0.4

-0.2

0

0.2

0.4

0.6

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

France: TOT to P

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Germany: CA to P

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Germany: TOT to P

0

1

2

3

4

5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Germany: RER to P

-0.6-0.4-0.2

00.20.40.60.8

11.21.4

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Italy: CA to P

-0.25

-0.2

-0.15

-0.1

-0.05

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Italy: TOT to P

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Italy: RER to P

00.20.40.60.8

11.21.41.61.8

2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Japan: CA to P

-0.16

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Japan: TOT to P

-1

-0.5

0

0.5

1

1.5

2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Japan: RER to P

-1

-0.5

0

0.5

1

1.5

2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

UK: RER to P

-1.4-1.2-1

-0.8-0.6-0.4-0.2

00.20.40.6

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

UK: CA to P

-0.09

-0.08

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

UK: TOT to P

0

0.5

1

1.5

2

2.5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

US: RER to P

0

0.5

1

1.5

2

2.5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

US: CA to P

-0.16

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

US: TOT to P

0

0.5

1

1.5

2

2.5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Figure 3

Plots depict the response of the CA, TOT, and RER to a productivity shock in the nontraded goods sector (NP). Solid lines are point estimates, and dashed lines represent the two standard deviation confidence interval of impulse responses from 1000 bootstrapping simulations.

Impulse Response Functions of CA, TOT, and RER to NP (SVAR Model 1)

Canada: CA to NP

-0.03

-0.02

-0.01

0

0.01

0.02

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Canada: TOT to NP

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Canada: RER to NP

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

France: CA to NP

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

France: RER to NP

-2

-1.5

-1

-0.5

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

France: TOT to NP

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Germany: CA to NP

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Germany: TOT to NP

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Germany: RER to NP

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Italy: CA to NP

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Italy: TOT to NP

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Italy: RER to NP

-5

-4

-3

-2

-1

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Japan: CA to NP

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Japan: TOT to NP

-0.5

0

0.5

1

1.5

2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Japan: RER to NP

-6

-5

-4

-3

-2

-1

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

UK: RER to NP

-7

-6

-5

-4

-3

-2

-1

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

UK: CA to NP

-0.02

-0.015

-0.01

-0.005

0

0.005

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

UK: TOT to NP

-1

-0.5

0

0.5

1

1.5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

US: CA to NP

-0.04

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

US: TOT to NP

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

US: RER to NP

-6

-5

-4

-3

-2

-1

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Horizon P M NP Horizon P M NPCanada France

1 0.990 0.003 0.007 1 0.892 0.007 0.102(0.031) (0.019) (0.020) (0.196) (0.116) (0.143)

2 0.989 0.004 0.007 2 0.915 0.005 0.080(0.049) (0.035) (0.019) (0.181) (0.105) (0.130)

3 0.990 0.004 0.006 3 0.925 0.003 0.072(0.049) (0.036) (0.019) (0.171) (0.095) (0.124)

4 0.990 0.003 0.007 4 0.936 0.004 0.061(0.048) (0.034) (0.019) (0.161) (0.090) (0.115)

8 0.991 0.003 0.006 8 0.959 0.003 0.039(0.044) (0.033) (0.016) (0.126) (0.069) (0.089)

12 0.993 0.003 0.005 12 0.969 0.003 0.029(0.035) (0.026) (0.013) (0.106) (0.057) (0.074)

16 0.994 0.002 0.004 16 0.975 0.002 0.023(0.030) (0.022) (0.011) (0.092) (0.050) (0.064)

20 0.995 0.002 0.003 20 0.980 0.002 0.019(0.026) (0.019) (0.009) (0.081) (0.043) (0.056)

Germany Italy1 0.950 0.049 0.001 1 0.915 0.001 0.084

(0.060) (0.058) (0.011) (0.160) (0.136) (0.053)2 0.951 0.047 0.002 2 0.931 0.010 0.059

(0.058) (0.057) (0.009) (0.150) (0.133) (0.039)3 0.958 0.035 0.007 3 0.945 0.007 0.047

(0.051) (0.049) (0.008) (0.141) (0.127) (0.033)4 0.959 0.028 0.013 4 0.944 0.015 0.041

(0.048) (0.045) (0.008) (0.146) (0.134) (0.030)8 0.979 0.014 0.007 8 0.964 0.010 0.026

(0.030) (0.028) (0.005) (0.082) (0.076) (0.015)12 0.986 0.009 0.005 12 0.974 0.007 0.020

(0.021) (0.020) (0.004) (0.063) (0.059) (0.011)16 0.990 0.007 0.003 16 0.979 0.005 0.015

(0.016) (0.015) (0.003) (0.053) (0.049) (0.009)20 0.992 0.005 0.003 20 0.983 0.004 0.013

(0.013) (0.012) (0.002) (0.045) (0.042) (0.008)

Japan UK1 0.426 0.517 0.056 1 0.920 0.014 0.067

(0.129) (0.125) (0.012) (0.063) (0.060) (0.016)2 0.476 0.489 0.035 2 0.934 0.010 0.056

(0.118) (0.115) (0.007) (0.052) (0.049) (0.013)3 0.553 0.421 0.027 3 0.927 0.008 0.065

(0.102) (0.100) (0.005) (0.050) (0.046) (0.013)4 0.630 0.350 0.020 4 0.940 0.007 0.053

(0.085) (0.084) (0.004) (0.044) (0.041) (0.011)8 0.815 0.171 0.014 8 0.960 0.006 0.034

(0.045) (0.044) (0.002) (0.032) (0.031) (0.007)12 0.871 0.119 0.010 12 0.971 0.005 0.025

(0.032) (0.031) (0.001) (0.024) (0.023) (0.005)16 0.900 0.093 0.008 16 0.977 0.004 0.019

(0.025) (0.025) (0.001) (0.020) (0.018) (0.004)20 0.919 0.075 0.006 20 0.981 0.003 0.016

(0.021) (0.020) (0.001) (0.016) (0.015) (0.004)

US1 0.588 0.308 0.104

(0.112) (0.080) (0.057)2 0.686 0.238 0.076

(0.075) (0.052) (0.037)3 0.740 0.204 0.056

(0.059) (0.042) (0.028)4 0.802 0.149 0.049

(0.046) (0.030) (0.024)8 0.905 0.071 0.024

(0.023) (0.014) (0.012)12 0.942 0.043 0.015

(0.014) (0.009) (0.007)16 0.959 0.031 0.011

(0.010) (0.007) (0.005)20 0.969 0.023 0.008

(0.008) (0.005) (0.004)

Table 4: The Forecast Variance Decomposition: CAThe numbers describe the proportion of the movements in the current account (CA) that are due to the productivity shock to the traded goods sector (P), the monetary shock (M), and the productivity shock to the nontraded goods sector (NP). The standard errors in parentheses are computed from 1000 bootstrapping simulations.

Horizon P M NP Horizon P M NPCanada France

1 0.033 0.899 0.069 1 0.073 0.847 0.080(0.283) (0.262) (0.060) (0.238) (0.216) (0.081)

2 0.036 0.900 0.064 2 0.169 0.772 0.059(0.285) (0.265) (0.055) (0.224) (0.205) (0.068)

3 0.037 0.917 0.046 3 0.179 0.771 0.050(0.282) (0.270) (0.033) (0.223) (0.206) (0.060)

4 0.049 0.917 0.034 4 0.254 0.690 0.056(0.280) (0.272) (0.023) (0.207) (0.189) (0.066)

8 0.065 0.917 0.018 8 0.310 0.649 0.041(0.279) (0.275) (0.011) (0.199) (0.184) (0.054)

12 0.062 0.925 0.013 12 0.346 0.622 0.032(0.276) (0.274) (0.008) (0.199) (0.186) (0.047)

16 0.062 0.928 0.010 16 0.361 0.613 0.026(0.275) (0.274) (0.006) (0.198) (0.186) (0.041)

20 0.061 0.930 0.008 20 0.372 0.606 0.022(0.275) (0.274) (0.005) (0.198) (0.188) (0.036)

Germany Italy1 0.214 0.770 0.016 1 0.001 0.996 0.003

(0.308) (0.305) (0.012) (0.196) (0.193) (0.007)2 0.222 0.758 0.020 2 0.008 0.988 0.004

(0.309) (0.306) (0.011) (0.201) (0.198) (0.007)3 0.237 0.749 0.014 3 0.019 0.975 0.006

(0.309) (0.307) (0.009) (0.205) (0.202) (0.007)4 0.282 0.707 0.011 4 0.022 0.971 0.008

(0.310) (0.308) (0.007) (0.207) (0.203) (0.007)8 0.326 0.668 0.006 8 0.020 0.975 0.005

(0.313) (0.311) (0.004) (0.207) (0.205) (0.004)12 0.352 0.644 0.004 12 0.017 0.980 0.003

(0.314) (0.313) (0.003) (0.207) (0.205) (0.003)16 0.367 0.630 0.003 16 0.016 0.982 0.002

(0.315) (0.314) (0.002) (0.207) (0.206) (0.002)20 0.377 0.621 0.002 20 0.015 0.983 0.002

(0.315) (0.315) (0.002) (0.207) (0.206) (0.002)

Japan UK1 0.031 0.949 0.020 1 0.019 0.917 0.064

(0.233) (0.232) (0.005) (0.301) (0.299) (0.009)2 0.037 0.940 0.023 2 0.023 0.900 0.077

(0.231) (0.230) (0.005) (0.301) (0.299) (0.011)3 0.043 0.929 0.028 3 0.028 0.876 0.096

(0.229) (0.228) (0.006) (0.300) (0.299) (0.014)4 0.049 0.917 0.034 4 0.037 0.836 0.127

(0.229) (0.227) (0.008) (0.300) (0.299) (0.019)8 0.079 0.863 0.058 8 0.050 0.774 0.177

(0.235) (0.232) (0.014) (0.301) (0.299) (0.029)12 0.110 0.810 0.080 12 0.060 0.722 0.218

(0.246) (0.241) (0.019) (0.302) (0.300) (0.037)16 0.141 0.744 0.115 16 0.066 0.688 0.247

(0.248) (0.241) (0.025) (0.303) (0.302) (0.042)20 0.213 0.605 0.182 20 0.068 0.679 0.253

(0.269) (0.255) (0.041) (0.304) (0.302) (0.043)

US1 0.199 0.735 0.066

(0.246) (0.214) (0.070)2 0.165 0.792 0.043

(0.257) (0.232) (0.052)3 0.193 0.773 0.034

(0.250) (0.230) (0.041)4 0.237 0.735 0.028

(0.232) (0.216) (0.032)8 0.326 0.660 0.014

(0.200) (0.192) (0.015)12 0.370 0.621 0.009

(0.184) (0.179) (0.009)16 0.396 0.598 0.006

(0.174) (0.171) (0.006)20 0.412 0.584 0.005

(0.169) (0.166) (0.005)

Table 5: The Forecast Variance Decomposition: TOT

The numbers describe the proportion of the movements in the terms of trade (TOT) that are due to the productivity shock to the traded goods sector (P), the monetary shock (M), and the productivity shock to the nontraded goods sector (NP). The standard errors in parentheses are computed from 1000 bootstrapping simulations.

Horizon P M NP Horizon P M NPCanada France

1 0.008 0.003 0.990 1 0.058 0.318 0.624(0.227) (0.222) (0.204) (0.182) (0.211) (0.227)

2 0.006 0.003 0.991 2 0.060 0.280 0.660(0.227) (0.223) (0.205) (0.176) (0.214) (0.233)

3 0.013 0.008 0.979 3 0.058 0.225 0.717(0.229) (0.223) (0.204) (0.166) (0.214) (0.239)

4 0.026 0.010 0.963 4 0.047 0.199 0.754(0.232) (0.222) (0.202) (0.156) (0.215) (0.241)

8 0.049 0.081 0.870 8 0.034 0.195 0.771(0.240) (0.204) (0.209) (0.140) (0.221) (0.242)

12 0.054 0.111 0.835 12 0.026 0.195 0.779(0.242) (0.199) (0.215) (0.133) (0.226) (0.244)

16 0.058 0.123 0.820 16 0.021 0.193 0.786(0.244) (0.197) (0.220) (0.129) (0.229) (0.245)

20 0.059 0.130 0.812 20 0.018 0.193 0.790(0.245) (0.196) (0.223) (0.126) (0.231) (0.246)

Germany Italy1 0.007 0.008 0.985 1 0.163 0.007 0.831

(0.240) (0.225) (0.212) (0.198) (0.141) (0.144)2 0.005 0.006 0.990 2 0.144 0.018 0.838

(0.241) (0.227) (0.209) (0.194) (0.142) (0.133)3 0.005 0.005 0.990 3 0.117 0.023 0.861

(0.239) (0.227) (0.211) (0.193) (0.143) (0.125)4 0.005 0.005 0.990 4 0.100 0.032 0.868

(0.240) (0.226) (0.203) (0.193) (0.144) (0.120)8 0.030 0.004 0.966 8 0.080 0.060 0.860

(0.243) (0.222) (0.186) (0.195) (0.146) (0.114)12 0.047 0.003 0.950 12 0.077 0.067 0.857

(0.245) (0.220) (0.183) (0.195) (0.147) (0.114)16 0.055 0.002 0.943 16 0.074 0.071 0.856

(0.247) (0.220) (0.183) (0.196) (0.147) (0.113)20 0.061 0.002 0.937 20 0.072 0.074 0.855

(0.248) (0.220) (0.182) (0.196) (0.147) (0.113)

Japan UK1 0.106 0.366 0.528 1 0.094 0.382 0.524

(0.248) (0.220) (0.177) (0.200) (0.203) (0.060)2 0.067 0.390 0.543 2 0.085 0.341 0.574

(0.245) (0.228) (0.165) (0.203) (0.205) (0.063)3 0.052 0.425 0.523 3 0.085 0.324 0.591

(0.245) (0.234) (0.158) (0.204) (0.207) (0.065)4 0.043 0.435 0.522 4 0.082 0.305 0.613

(0.247) (0.237) (0.156) (0.206) (0.208) (0.066)8 0.025 0.389 0.586 8 0.054 0.298 0.648

(0.252) (0.235) (0.161) (0.205) (0.207) (0.068)12 0.019 0.352 0.630 12 0.043 0.292 0.666

(0.257) (0.233) (0.172) (0.206) (0.207) (0.070)16 0.015 0.337 0.648 16 0.037 0.288 0.675

(0.260) (0.234) (0.180) (0.206) (0.207) (0.071)20 0.012 0.330 0.658 20 0.034 0.286 0.681

(0.263) (0.236) (0.184) (0.206) (0.208) (0.071)

US1 0.175 0.013 0.811

(0.319) (0.078) (0.306)2 0.138 0.063 0.799

(0.321) (0.083) (0.307)3 0.134 0.069 0.797

(0.322) (0.085) (0.309)4 0.126 0.081 0.793

(0.323) (0.088) (0.310)8 0.118 0.066 0.816

(0.324) (0.087) (0.312)12 0.115 0.060 0.825

(0.324) (0.087) (0.312)16 0.113 0.056 0.830

(0.325) (0.086) (0.312)20 0.112 0.055 0.833

(0.325) (0.086) (0.312)

Table 6: The Forecast Variance Decomposition: RER

The numbers describe the proportion of the movements in the real exchange rate (RER) that are due to the productivity shock to the traded goods sector (P), the monetary shock (M), and the productivity shock to the nontraded goods sector (NP). The standard errors in parentheses are computed from 1000 bootstrapping simulations.