# Stochastic trends and balance of payments crises

Mohamed Ben Abdallah, Taoufik Rahji

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Mohamed Ben Abdallah, Taoufik Rahji. Stochastic trends and balance of payments crises. 2000. halshs-03773481

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Stochastic trends and balance of payments crises

Mohamed BEN ABDALLAH

Taoufik RAJHI

2000.68
Stochastic trends and balance of payments crises

MAI 2000

(preliminary paper)

Mohamed BEN ABDALLAH* and Taoufik RAJHI**

*(TEAM) Paris-I University, E-MAIL: benmoha@univ-paris1.fr

**(LEO) Orleans University, E-MAIL: rajhi@univ-paris1.fr

Paper presented at the International Conference: RESHAPING THE ARCHITECTURE OF THE INTERNATIONAL FINANCIAL SYSTEM (SIENA 2000)
# 1. Introduction

The last years witnessed the multiplication of financial currency crisis in several developed and developing countries: the debt crisis in 1980s, the series of crises that have hit the European currency regime between 1992 and 1993, the Mexican devaluation in 1994, the Asian crisis in 1997 that touched not only Asia but equally Latin America and industrialised countries. This last crisis constituted the starting point of a renewal interest in the theoretical debate on the currency crises between two points of view :<fnref n="1" />:

Radelet and Sacks(1998a) and Obstfeld (1998) see that the causes of the currency crisis is a sudden shift in market expectations and confidence : the depth of the crisis should not be attributed to a sharp deterioration in fundamentals² but to realisation of a "bad equilibrium". In the self-fulfilling model of crisis initiated by Obstfeld (1994), balance of payments crises can be unjustified by fundamentals. Speculative attack can materialise where individual investors believe that the currency regime is in no "imminent danger" (case of self confirming pessimism).

In the second point of view supported by Krugman (1998a) the Asian crisis reflect an unsustainable deterioration in the macroeconomic fundamentals, as well as, poor policies in the Asian countries. Thus the canonical currency-crisis model initiated by Krugman (1979) and refined by Flood and Garber (1984) explains such crisis as the result of a fundamental inconsistency between domestic policies, typically the persistence of money-financed budget deficits and the attempt to maintain a fixed exchange rate, which makes the long-run survival of the fixed rate impossible. In that sense, the crisis is driven by economic fundamentals. The crisis of payments happens because the fundamentals have reached the point at which the exchange rate would have collapsed in the absence of speculative attack.

Coresetti, Pesenti, and Roubini, (1998) suggest that poor economic fundamentals were certainly crucial elements in the genesis of the crisis and its spreading across countries (over investment, growing current account imbalances, foreign debt accumulation...)

In this paper we investigate the importance of domestic nominal and real shocks on the fluctuations of the exchange rate and the current account balance in four countries: South Korea, Indonesia, Thailand and Mexico. We also analyse the effect of the budgetary deficit and the monetary policy on their exchange rate and their current account balance. We use the

<footnote n="1">¹ For further details about the causes of Asian crises, see Coresetti, Pesenti and Roubini, (1998).</footnote>
recently developed multivariate cointegration analysis followed by the model of common
trends<fnref n="3" />, the vector error correlation modelling, the impulse response functions and the
variance decompositions.

We assume that the exchange rate and the current account are integrated of order 1 (non stationary) and that transitory shocks have non-long effect upon it. We use this as an identifying assumption. We associate the transitory shock to a foreign innovation.

The results presented in this paper suggest that the Asian crisis is in fact a combination
of a domestic shock (weak fundamentals) and externals shocks. We also show that Asian
countries have exposed their economy to external shock that have rendered their economy
vulnerable to them. Consequently, we suggest that we must search the causes of Asian crises
in the external shocks that have hit the Asian economy, like the stagnation of the Japanese
economy in the 1990s that led to a slowdown of the growth of exports of the Asian economies
to Japan, the sharp appreciation of the US. Dollar relative to the yen and the European
currencies since the second half of 1995 that led to a worsening of cost-competitiveness in the
Asian countries whose currencies were pegged to the dollar, the fall in the demand for
semiconductors and the fluctuations of the terms of trade that caused a significant decrease of
export growth in several Asian countries.

The remainder of the paper is organised as follow, section I provides the time series
properties of the data, section II presents the empirical study results. Section III presents the
effects of fiscal and monetary policy in the four variables of interest. Our conclusions are
contained in section IV.

## 2. The time series properties of the data

The model consists of six variables: the logarithm of industrial products index (LIPI)
which is used as a proxy for real output, the index for expansionary fiscal policy (DP), the
logarithm of quasi money (LOG QUASI MONEY), the interest rate differential measured as
the difference between the domestic rate and the federal funds rate ($i^D - i^{US}$), the nominal
exchange rate (TCN) defined as units of domestic currency per US dollar, and the current

<footnote n="2">² The currency crisis stems from especially to a deterioration of three fundamentals : over evaluation of the exchange rate, unbearable level of current account imbalances and foreign shocks like a decrease in the American interest rate.</footnote>

<footnote n="3">³ We use the approach developed by Warner (1990) "common trends models in open economy" and the estimation algorithm written by the same author.</footnote>
account balance (CA)<fnref n="4" />. The main data source is the international financial statistics published by the IMF for South Korea, Mexico, Indonesia and Thailand.

The data are quarterly seasonally unadjusted, covering the period before the Asian crisis 1980:01, 1997:02. For each country the expansionary fiscal policy index and the logarithm of the money supply are assumed to be exogenous variables in the system. Hence, the $X_t$ vector used has the following form [LOG(IPI), $(i^D - i^{US})$, TCN, CA].

## 2.1 Tests for the deterministic trends:

The results provided by the unit root tests, the cointegration, and common trends models are sensitive to the presence of a deterministic trend. The procedure developed by Stock and Watson (1989) is conducted for this purpose. First we check the presence of the deterministic trend in level. The variable in level is regressed upon a constant and a sufficient number of its own lagged values. This is then followed by a second regression in which a linear time trend is added. The test is repeated on the first differences of the series. All variables in first differences, except the $(i^D - i^{US})$, possess a constant but none of them seems to have a significant time trend.

## 2.2 Unit root tests:

The unit property of the series is crucial for cointegration and common trends models analysis. It is examined by the standard augmented Dickey-Fuller (ADF) and Phillips Perron (PP) tests. It is known that the results of these tests often depend on the number of included lags. Campbell and Perron (1991) employed an elimination procedure to identify the optimal lag structure. Each equation is initially estimated with twelve lags on the dependent variable. If the last included lag is significant, the number of lags is kept at twelve. Otherwise, the number of lags is reduced by one and the equation was re estimated, until a significant lagged dependent variable is found.

Results tend to indicate that we can not reject the presence of a unit root for any of the variables considered i.e., variables were appeared to be non stationary when taken as levels

<footnote n="4">⁴ The current account balance seems to us the part of the balance of payment the most important for several reasons: it measures net moves of effective resources between the national economy and the remainder of the world. It represents the difference between the national income and the domestic absorption.</footnote>
but stationary after "first difference". For clarity reason the results of these test are not presented here.

## 2.3 Multivariate cointegration analysis:

The multivariate cointegration technique developed by Johanson and Jeselius (1990) is applied to the system of four variables which are integrated of order one. The uniform lag structure of the system is set up through a search process using the likelihood Ratio test with a potential lag length of one to eight. The null hypothesis is a system of variables generated from Gaussian var with $p_0$ lags against the alternative specification of $p_1$ lags, where $p_1 > p_0$. The statistic is asymptotically distributed as $X(2)$ with $n^2(p_1 - p_0)$ degrees of freedom<fnref n="5" />.

The results of the test for the number of cointegrating vectors are presented in table 1.

<table><caption>Table 1
Likelihood ratio tests for the number of cointegrating vectors</caption><thead><tr><th colspan="2">Hypothesis</th><th>KOREA</th><th>MEXIQUE</th><th>INDONESIE</th><th>THAILANDE</th></tr></thead><tbody><tr><td colspan="2">A. The maximum eigenvalue (λ<sub>max</sub>) criteria</td><td></td><td></td><td></td><td></td></tr><tr><td>H<sub>0</sub></td><td>H<sub>1</sub></td><td></td><td></td><td></td><td></td></tr><tr><td>r = 0</td><td>r ≤ 1</td><td>31.60</td><td>29.47</td><td>18.86</td><td>17.14</td></tr><tr><td>r ≤ 1</td><td>r ≤ 2</td><td>13.72</td><td>14.97</td><td>17.67</td><td>13.39</td></tr><tr><td>r ≤ 2</td><td>r ≤ 3</td><td>11.23*</td><td>9.04*</td><td>8.48*</td><td>10.60*</td></tr><tr><td>r ≤ 3</td><td>r = 4</td><td>1.77</td><td>0.59</td><td>1.49</td><td>2.71</td></tr><tr><td colspan="2">B. The trace criteria</td><td></td><td></td><td></td><td></td></tr><tr><td>r = 0</td><td>r ≤ 1</td><td>58.32</td><td>54.05</td><td>46.50</td><td>43.84</td></tr><tr><td>r ≤ 1</td><td>r ≤ 2</td><td>26.72</td><td>24.59</td><td>27.64</td><td>26.70</td></tr><tr><td>r ≤ 2</td><td>r ≤ 3</td><td>13.00*</td><td>9.62*</td><td>9.97*</td><td>13.31*</td></tr><tr><td>r ≤ 3</td><td>r = 4</td><td>1.77</td><td>0.59</td><td>1.49</td><td>2.71</td></tr></tbody></table>

r indicates the number of cointegrating vectors. The estimated model had three lags, an drift and four seasonal dummy variables. The asterisks indicate the following levels of significance: * 5% and ** 10%. The critical values are provided by the logical CATS output based on λ<sub>max</sub> and trace.

For the four countries of interest, the results presented above accept the existent of two cointegrating vectors at the 5% significance level. This implies that we have two common stochastic trends among the six variables (the number of common trends is equal to the number of variables minus the number of cointegrating vectors)<fnref n="6" />

<footnote n="5">⁵ To determine the lags length, p we can use the Akaike information criterion (AIC), the Schwartz information criterion (SIC) and the iterated lag criterion (ILC). Paulsen (82) shown that the Akaike criterion, however, is not consistent and tends to overestimate the true lag order.</footnote>

<footnote n="6">⁶ Stock and Watson (1988) derive the common trends models from the Would representation under the assumption of *k* common trends, and shows that if there are *r* cointegrating vectors, *k* = *n* - *r*</footnote>
### 3. The estimated common trends model

#### 3.1 The representation of Common trends model<fnref n="7" />

#### 3.2 Identification of shocks :

There are two common stochastic trends in the data that we identify respectively as a domestic real trend, $τ_R$, and a domestic nominal trend $τ_N$. In order to interpret the trends in this way, we assume that money is neutral in the long run, i.e. nominal shocks have no long run effects on real variables, whereas domestic real shock affect all variables in $X_t$. We have also two transitory shocks that we identify as a foreign shocks.

Given the ordering of the variables [LOG(IPI), $(i^D - i^{US})$, TCN, CA] and the ordering $τ_R, τ_N$ of the common trends, these restrictions imply that the elements in the second column of $A_0$<fnref n="8" /> should be zero. The $A_0$ matrix must satisfy $α' A_0 = 0$ in order for $α'_{X_t}$ to be stationary.

For each country we specify the $A_0$ matrix as :

$$A_0 = \begin{bmatrix} 1 & 0 \\ 1 & 1 \\ a_{31} & a_{32} \\ a_{41} & a_{42} \end{bmatrix}$$

The zero in the first row, second column reflect the assumption of nominal neutrality.

The parameters of the estimated common trends models are presented in Table2 :

<footnote n="7">⁷ See the Annexe.</footnote>

<footnote n="8">⁸ $A_0$ is an n\*k long-run impact matrix with known parameters (characterising the effects of each of the stochastic trends on the respective elements of the $X_t$ vectors).</footnote>
<caption>Table 2
The estimated common trends models based on the cointegrating vectors. The estimated parameters presented below are those of the long-run using the ordering [In IPI, DTI, TCN, CA]</caption>

$$
\begin{array}{l}
\left[ \begin{array}{c} \text{InIPI} \\ (i^D - i^{US}) \\ \text{TCN} \\ \text{CA} \end{array} \right] = X_0 + \begin{bmatrix} \text{KOREA} \\ \begin{bmatrix} 0.0234 & 0.0000 \\ (0.4502) & (-) \\ 0.3585 & 0.5770 \\ (0.2776) & (0.2233) \\ -11.9828 & -34.0153 \\ (2.0709) & (1.7160) \\ -0.1158 & 0.8813 \\ (0.3289) & (0.2762) \end{bmatrix} \begin{bmatrix} \tau_R \\ \tau_N \end{bmatrix} + \hat{u}_t \end{array} \\
\\
\left[ \begin{array}{c} \text{InIPI} \\ (i^D - i^{US}) \\ \text{TCN} \\ \text{CA} \end{array} \right] = X_0 + \begin{bmatrix} \text{INDONESIA} \\ \begin{bmatrix} 0.0178 & 0.0000 \\ (0.0400) & (-) \\ -0.0020 & 0.1291 \\ (0.1280) & (0.1077) \\ -2.7434 & 60.1833 \\ (2.7659) & (2.3255) \\ -0.6254 & 1.4861 \\ (0.4438) & (0.3653) \end{bmatrix} \begin{bmatrix} \tau_R \\ \tau_N \end{bmatrix} + \hat{u}_t \end{array} \\
\\
\left[ \begin{array}{c} \text{InIPI} \\ (i^D - i^{US}) \\ \text{TCN} \\ \text{CA} \end{array} \right] = X_0 + \begin{bmatrix} \text{MEXICO} \\ \begin{bmatrix} 0.0484 & 0.0000 \\ (0.0668) & (-) \\ -16.0706 & 4.7489 \\ (1.2641) & (0.6536) \\ 0.7781 & 0.4522 \\ (0.3044) & (0.2014) \\ -8.8507 & 10.1867 \\ (1.2408) & (0.9573) \end{bmatrix} \begin{bmatrix} \tau_R \\ \tau_N \end{bmatrix} + \hat{u}_t \end{array} \\
\\
\left[ \begin{array}{c} \text{THAILAND} \\ \begin{bmatrix} 0.0323 & 0.0000 \\ (0.0551) & (-) \\ -0.4756 & 3.5410 \\ (0.6852) & (0.5715) \\ -0.8176 & 0.2106 \\ (0.1697) & (0.1393) \\ 0.1032 & -1.4072 \\ (0.4311) & (0.3602) \end{bmatrix} \begin{bmatrix} \tau_R \\ \tau_N \end{bmatrix} + \hat{u}_t \end{array} \right]
\end{array}
$$

Terms in parenthesis are the absolute standard errors.

The first and second column of each matrix above correspond to the estimated parameters in respect of domestic real and nominal shocks respectively. These interpretations are based on the restrictions of neutrality of money with respect to real variables (innovations to the nominal trend have no long run effect on real variables).

Domestic nominal trends have no real impacts in the long run as predicted by theory. Their effects on the interest rate differential have the wrong sign in the four considered cases. In the exception of the case of Korea where the effect of the nominal shocks on the exchange rate has a wrong sign, the nominal shocks seem to depreciate domestic currency and improve current account significantly according with the Marshall-learner condition. This result is consistent with EICHEBAM and EVANS (1995) that find that contractionary shocks to U.S. monetary policy are followed by sharp increases in U.S. interest rates, and sharp persistent decreases in the spread between foreign and U.S interest rates. They also find that the same shocks lead to sharp, persistent appreciation in U.S. nominal and real exchange rates.

For the four considered countries, positive domestic real trends appreciate the domestic currency and worsen the current account balance significantly. The real shocks decrease the interest rate differential significantly only for Mexico.

Lee and Menzie (1999) examine the behaviour of US, Canada, UK, Japan, France and Italy and show that a permanent shock, which they interpret as a technological innovation, induces a permanent appreciation of the real exchange rate and has some effect on the current account, although it is often statistically insignificant. Nevertheless, improvement in the current account is somewhat difficult to explain in the context of an inter-temporal model.
The permanent positive productivity shocks should not induce any effect in the absence of rigidities.

Obstfeld and Rogoff (88) show that an unexpected positive shock on output causes an unexpected rise in the current account surplus when the shock is temporary, because people smooth expected consumption inter-temporally through asset accumulation. A permanent shock has no current account effect because consumption remains level (in expectation) if people simply adjust consumption by the full innovation to output.

We review in the next section the analysis of this stochastic shocks in the short, medium and long term, using the impulse-response functions as well the forecast error decomposition.

### 3.3 Analysis in term of impulse-response functions

In Figures 1-2 the responses to innovations to the stochastic trend "permanent shocks" (domestic real and nominal shocks) are displayed, along with 95 per cent confidence intervals.

Looking first at the effects of domestic technological (supply) figure 1. Positive real domestic shocks tend to improve the exchange rate and worsen the current account balance over the same period for all the four countries considered. The effect of these real shocks on the interest rate differential is negative only for the case of Mexico where domestic technology shocks improve the interest rate differential over the first seven quarters and decrease it afterwards. The response of IPI to real shocks is positive and similar in the four countries. These results are consistent with the international economic theory.

The response functions for innovations to the nominal domestic trend are shown in figure 2. The responses of output to a nominal shocks are negative after two years in the case of Mexico and after one year in the case of Indonesia. The restriction of long run neutrality does not seem to be in conflict with the data of the four countries.

In accordance with Marshall-Lerner condition the nominal trends depreciate domestic currencies and improve current account balances for the four countries. We find the J-curve response of the current account balance to nominal shocks, such the impact effect is negative with the improvement only according with a lag (two quarters in the case of Korea and Indonesia and four quarters in the case of Thailand).

This results are consistent with Lane (1998) that estimates a VAR model using as identification assumption that monetary shocks have no long-run effect on the current account and with Lee and Chinn (1998) rather adopt the identification restriction that monetary shocks
FIGURE.1 : Impulse-response functions to real domestic shock ($\tau_R$) following a one-standard deviation shock with 95% confidence bounds.

KOREA

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

INDONESIE
MEXIQUE

<figure><img src="image_10.png" /><figcaption>RESP IN (i^D - i^US) TO INNOV PERM 1</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>RESP IN TCN TO INNOV PERM 1</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>RESP IN CA TO INNOV PERM 1</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>RESP IN LOG(IP1) TO INNOV PERM 1</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>RESP IN (i^D - i^US) TO INNOV PERM 1</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>RESP IN TCN TO INNOV PERM 1</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>RESP IN CA TO INNOV PERM 1</figcaption></figure>

<figure><img src="image_17.png" /></figure>
<caption>FIGURE.2 : Impulse-response functions to nominal domestic shock ($τ_N$) following a one-standard deviation shock with 95% confidence bounds.</caption>

KOREA

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

INDONESIE

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>
MEXIQUE

RESP IN LOG(IPI) TO INNOV PERM 2

<figure><img src="image_22.png" /></figure>

RESP IN TCN TO INNOV PERM 2

<figure><img src="image_23.png" /></figure>

RESP IN $(i^D - i^{US})$ TO INNOV PERM 2

<figure><img src="image_24.png" /></figure>

RESP IN CA TO INNOV PERM 2

<figure><img src="image_25.png" /></figure>

THAILANDE

RESP IN LOG(IPI) TO INNOV PERM 2

<figure><img src="image_26.png" /></figure>

RESP IN TCN TO INNOV PERM 2

<figure><img src="image_27.png" /></figure>

RESP IN $(i^D - i^{US})$ TO INNOV PERM 2

<figure><img src="image_28.png" /></figure>

RESP IN CA TO INNOV PERM 2

<figure><img src="image_29.png" /></figure>
have no long-run impact on the real exchange rate. Each of these papers find that positive nominal shocks tend to improve the current account position.

We now turn to the relative importance of the various disturbances: domestic permanent shocks and transitory shocks. In order to explain the magnitude of the effects, we use the decompositions of variance shown in Tables 3.4 and 5.

### 3.3 Decompositions of variance

#### Real domestic permanent shocks

<caption>Table 3</caption>

Ratio of s steps ahead error variance of $X_t$ accounted for by the domestic real trend ($\tau_R$).

<table><thead><tr><th rowspan="2">s</th><th colspan="3">KOREA</th><th colspan="3">MEXICO</th></tr><tr><th>In IPI</th><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>In IPI</th><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th></tr></thead><tbody><tr><td>1</td><td>0.6732</td><td>0.1700</td><td>0.0007</td><td>0.0012</td><td>0.5209</td><td>0.0017</td><td>0.0136</td><td>0.6602</td></tr><tr><td>2</td><td>0.7329</td><td>0.2367</td><td>0.0726</td><td>0.0008</td><td>0.5782</td><td>0.0023</td><td>0.0049</td><td>0.5061</td></tr><tr><td>3</td><td>0.7339</td><td>0.2542</td><td>0.0631</td><td>0.0031</td><td>0.4805</td><td>0.0030</td><td>0.0147</td><td>0.4249</td></tr><tr><td>4</td><td>0.7775</td><td>0.2568</td><td>0.0648</td><td>0.0098</td><td>0.4548</td><td>0.0030</td><td>0.0202</td><td>0.3932</td></tr><tr><td>10</td><td>0.8885</td><td>0.2770</td><td>0.0850</td><td>0.0168</td><td>0.4866</td><td>0.0218</td><td>0.0835</td><td>0.3368</td></tr><tr><td>15</td><td>0.9244</td><td>0.2830</td><td>0.2539</td><td>0.0169</td><td>0.6126</td><td>0.0691</td><td>0.1659</td><td>0.3392</td></tr><tr><td>20</td><td>0.9426</td><td>0.2840</td><td>0.1012</td><td>0.0167</td><td>0.7014</td><td>0.1328</td><td>0.2441</td><td>0.3424</td></tr><tr><td>&infin;</td><td>1.0000</td><td>0.2785</td><td>0.1103</td><td>0.0169</td><td>1.0000</td><td>0.9196</td><td>0.7474</td><td>0.4301</td></tr></tbody></table><table><thead><tr><th rowspan="2"></th><th colspan="3">INDONESIA</th><th colspan="3">THAILAND</th></tr><tr><th>In IPI</th><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>In IPI</th><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th></tr></thead><tbody><tr><td>1</td><td>0.9762</td><td>0.0276</td><td>0.0314</td><td>0.0012</td><td>0.4714</td><td>0.3093</td><td>0.0403</td><td>0.0813</td></tr><tr><td>2</td><td>0.9612</td><td>0.0421</td><td>0.0155</td><td>0.0018</td><td>0.4333</td><td>0.1580</td><td>0.0661</td><td>0.0534</td></tr><tr><td>3</td><td>0.9497</td><td>0.0421</td><td>0.0159</td><td>0.0222</td><td>0.4052</td><td>0.2000</td><td>0.0940</td><td>0.0602</td></tr><tr><td>4</td><td>0.9496</td><td>0.0444</td><td>0.0138</td><td>0.0502</td><td>0.4145</td><td>0.1969</td><td>0.1322</td><td>0.0652</td></tr><tr><td>10</td><td>0.9651</td><td>0.0367</td><td>0.0690</td><td>0.1559</td><td>0.5104</td><td>0.2683</td><td>0.2871</td><td>0.0674</td></tr><tr><td>15</td><td>0.9750</td><td>0.0367</td><td>0.0050</td><td>0.1046</td><td>0.5160</td><td>0.3272</td><td>0.3723</td><td>0.0769</td></tr><tr><td>20</td><td>0.9796</td><td>0.0364</td><td>0.0043</td><td>0.1140</td><td>0.5618</td><td>0.3729</td><td>0.3924</td><td>0.0782</td></tr><tr><td>&infin;</td><td>1.0000</td><td>0.0002</td><td>0.0020</td><td>0.1504</td><td>0.9999</td><td>0.0177</td><td>0.1308</td><td>0.0053</td></tr></tbody></table>

As can be seen in table (3) the influence of real shocks on the interest rate differential, exchange rate, and the current account balance is apparently negligible in the short as well as in the long run except for Mexico where the domestic real shocks explain on average 40% of the variance of the current account balance. At a forecast horizon longer than five years around 70% of the variance of the exchange rate and the current account in Mexico can be accounted by shocks to real trend contrary to the case of the Korea and Indonesia. In all the countries the real domestic shocks account for a substantial proportion of the variance in output in the short as well as in the long run in accordance with the real business cycle theory.
## Nominal permanent shocks

<table><caption>Table 4
Ratio of s steps ahead error variance of $X_t$ accounted for by the domestic nominal trend ($\tau_N$).</caption><thead><tr><th rowspan="2">S</th><th rowspan="2">In IPI</th><th colspan="4">KOREA</th><th colspan="3">MEXICO</th></tr><tr><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th><th>In IPI</th><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th></tr></thead><tbody><tr><td>1</td><td>0.0013</td><td>0.2260</td><td>0.2922</td><td>0.7195</td><td>0.0033</td><td>0.7946</td><td>0.5340</td><td>0.2226</td></tr><tr><td>2</td><td>0.0024</td><td>0.1336</td><td>0.2922</td><td>0.7723</td><td>0.0839</td><td>0.8947</td><td>0.5014</td><td>0.4371</td></tr><tr><td>3</td><td>0.0022</td><td>0.1014</td><td>0.3661</td><td>0.7911</td><td>0.2351</td><td>0.9076</td><td>0.5533</td><td>0.5236</td></tr><tr><td>4</td><td>0.0018</td><td>0.0921</td><td>0.4491</td><td>0.8047</td><td>0.3215</td><td>0.8656</td><td>0.6152</td><td>0.5651</td></tr><tr><td>10</td><td>0.0026</td><td>0.1421</td><td>0.7272</td><td>0.8302</td><td>0.2857</td><td>0.8409</td><td>0.6822</td><td>0.6453</td></tr><tr><td>15</td><td>0.0022</td><td>0.2539</td><td>0.7996</td><td>0.8245</td><td>0.2127</td><td>0.8081</td><td>0.6610</td><td>0.6482</td></tr><tr><td>20</td><td>0.0017</td><td>0.3556</td><td>0.8300</td><td>0.8237</td><td>0.1645</td><td>0.7596</td><td>0.6237</td><td>0.6481</td></tr><tr><td>&infin;</td><td>0.0000</td><td>0.7214</td><td>0.8896</td><td>0.9830</td><td>0.0000</td><td>0.0803</td><td>0.2525</td><td>0.5698</td></tr></tbody></table><table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">INDONESIA</th><th colspan="3">THAILAND</th></tr><tr><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th><th>In IPI</th><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th></tr></thead><tbody><tr><td>1</td><td>0.0004</td><td>0.0193</td><td>0.8570</td><td>0.0833</td><td>0.2296</td><td>0.3741</td><td>0.0139</td><td>0.3994</td></tr><tr><td>2</td><td>0.0008</td><td>0.0285</td><td>0.9194</td><td>0.0777</td><td>0.1845</td><td>0.2306</td><td>0.0052</td><td>0.2254</td></tr><tr><td>3</td><td>0.0074</td><td>0.0283</td><td>0.9327</td><td>0.0861</td><td>0.1113</td><td>0.2345</td><td>0.0291</td><td>0.1688</td></tr><tr><td>4</td><td>0.0077</td><td>0.0838</td><td>0.9392</td><td>0.1124</td><td>0.0913</td><td>0.3008</td><td>0.0687</td><td>0.1469</td></tr><tr><td>10</td><td>0.0056</td><td>0.1693</td><td>0.9740</td><td>0.3520</td><td>0.0614</td><td>0.4290</td><td>0.1763</td><td>0.0925</td></tr><tr><td>15</td><td>0.0043</td><td>0.1755</td><td>0.9815</td><td>0.4750</td><td>0.0344</td><td>0.4327</td><td>0.2394</td><td>0.0919</td></tr><tr><td>20</td><td>0.0035</td><td>0.1837</td><td>0.9855</td><td>0.5451</td><td>0.0255</td><td>0.4167</td><td>0.2410</td><td>0.1032</td></tr><tr><td>&infin;</td><td>0.0000</td><td>0.9997</td><td>0.9979</td><td>0.8495</td><td>0.56<sup>E</sup>-05</td><td>0.9822</td><td>0.8691</td><td>0.9946</td></tr></tbody></table>

The role of the permanent nominal shocks is apparently negligible, in the short as well as in the long run in the case of output, the role of the permanent nominal shocks is practically null. On the other hand, it accounts for most of the variance in interest rate differential, exchange rate and the current account balance (see table 4). For example at one year horizon, nominal permanent shocks explain 70% per cent in Korea, 50% per cent in Mexico and 20% per cent in Indonesia and Thailand of the forecast error variance in exchange rate.

## Permanent versus transitory shocks<sup>9</sup>

<table><caption>Table 5
Ratio of s steps ahead error variance of $X_t$ accounted for by the two transitory shocks ($\tau_R$).</caption><thead><tr><th rowspan="2">S</th><th rowspan="2">In IPI</th><th colspan="4">KOREA</th><th colspan="3">MEXICO</th></tr><tr><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th><th>In IPI</th><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th></tr></thead><tbody><tr><td>1</td><td>0.3254</td><td>0.6038</td><td>0.7070</td><td>0.2791</td><td>0.4756</td><td>0.2035</td><td>0.4521</td><td>0.1171</td></tr><tr><td>2</td><td>0.2645</td><td>0.6295</td><td>0.6350</td><td>0.2267</td><td>0.3376</td><td>0.1028</td><td>0.4935</td><td>0.0566</td></tr><tr><td>3</td><td>0.2637</td><td>0.6441</td><td>0.5706</td><td>0.2056</td><td>0.2842</td><td>0.0891</td><td>0.4318</td><td>0.0513</td></tr><tr><td>4</td><td>0.2205</td><td>0.6510</td><td>0.4858</td><td>0.1854</td><td>0.2236</td><td>0.1311</td><td>0.3644</td><td>0.0415</td></tr><tr><td>10</td><td>0.1087</td><td>0.5807</td><td>0.1807</td><td>0.1528</td><td>0.2274</td><td>0.1371</td><td>0.2341</td><td>0.0177</td></tr><tr><td>15</td><td>0.0732</td><td>0.4620</td><td>0.1039</td><td>0.1584</td><td>0.1745</td><td>0.1226</td><td>0.1729</td><td>0.0124</td></tr><tr><td>20</td><td>0.0555</td><td>0.3601</td><td>0.0686</td><td>0.1594</td><td>0.1338</td><td>0.1075</td><td>0.1319</td><td>0.0093</td></tr><tr><td>&infin;</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr></tbody></table><table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">INDONESIA</th><th colspan="3">THAILAND</th></tr><tr><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th><th>In IPI</th><th>(i<sup>D</sup> - i<sup>US</sup>)</th><th>TCN</th><th>CA</th></tr></thead><tbody><tr><td>1</td><td>0.0066</td><td>0.9528</td><td>0.1114</td><td>0.9152</td><td>0.2987</td><td>0.3164</td><td>0.9455</td><td>0.5191</td></tr><tr><td>2</td><td>0.0378</td><td>0.9293</td><td>0.0649</td><td>0.9203</td><td>0.3820</td><td>0.6112</td><td>0.9286</td><td>0.7211</td></tr><tr><td>3</td><td>0.0427</td><td>0.9294</td><td>0.0512</td><td>0.8915</td><td>0.4834</td><td>0.5653</td><td>0.8767</td><td>0.7708</td></tr><tr><td>4</td><td>0.0425</td><td>0.8716</td><td>0.0468</td><td>0.8372</td><td>0.4940</td><td>0.5021</td><td>0.7989</td><td>0.7877</td></tr><tr><td>10</td><td>0.0288</td><td>0.7935</td><td>0.1505</td><td>0.2921</td><td>0.4280</td><td>0.3025</td><td>0.5364</td><td>0.8399</td></tr><tr><td>15</td><td>0.0205</td><td>0.7875</td><td>0.0133</td><td>0.4202</td><td>0.4493</td><td>0.2399</td><td>0.3880</td><td>0.8310</td></tr><tr><td>20</td><td>0.0166</td><td>0.7798</td><td>0.0100</td><td>0.3407</td><td>0.4125</td><td>0.2102</td><td>0.3665</td><td>0.8184</td></tr><tr><td>&infin;</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr></tbody></table>
The sources of fluctuations in these countries seem to be domestic but we can not deny the role of foreign shocks. Thus, the transitory shocks that we identify as foreign shocks account for about 60% to 80% of the forecast error variance in current account balance in the case of Indonesia and between 50% to 70% (27% to 47%) of the forecast error variance in the exchange rate in the case of Korea and Mexico respectively, at the one year horizon.

Transitory shocks play an important influence only on the interest rate differential, the exchange rate and the current account balance, although we must underline the important role of the two domestic permanent shocks on the fluctuation of the output. We notice that the forecast error variance explained by the transitory shocks is somewhat greater in the case of Asian countries than on the case of Mexico.

The tables above suggest that common domestic and external shocks hit the Asian economies, through to different degrees across countries. Thailand seems to be touched to a larger extent than the other countries by the foreign shocks, which explains why speculators bet against Thailand.

We can conclude for the countries of interest, especially the Asian countries, that the transitory shocks (foreign shocks) play an important role in exchange rate and the current account balance fluctuations. The Asian countries have exposed their industrial sector and their economy to the external shocks and that had made their economy vulnerable to external shocks.

# 4. The effects of fiscal and monetary policy

We analyse now the short run effects of budgetary deficit and monetary policy on the exchange rate and the current account. We estimate a vector error correlation model in which: the budgetary deficit and the log of money are introduced as exogenous variables in the model<fnref n="10" />.

The nature of the vector error correlation models do allows us to investigate only the short run effects that are presented in the following table6.

<footnote n="9">⁹ The sum of the two transitory shocks is 1 less the sum of the two permanent shocks.</footnote>

<footnote n="10">¹⁰ The budgetary deficit and the monetary policy are considered in literature on balance-of-payments crises as the means fundamentals that can affect the balance of payments crises.</footnote>
<caption>Table 6</caption>

The short-run effects of budgetary deficit and monetary policy on the $X_t$ vector, [In IPI, DTI, TCN, CA]; t-values are in parenthesis (the first and second column in each matrix refers to the effects of budgetary deficits and of money supply shocks respectively)

<table><thead><tr><th>KOREA</th><th>MEXICO</th></tr></thead><tbody><tr><td><table><tr><td>1.69E-6</td><td>0.4665</td></tr><tr><td>(1.1394)</td><td>(1.9277)</td></tr><tr><td>-7.83E-5</td><td>-3.9447</td></tr><tr><td>(-1.0791)</td><td>(-0.3339)</td></tr><tr><td>0.0004</td><td>-43.7828</td></tr><tr><td>(0.8827)</td><td>(-0.4808)</td></tr><tr><td>-0.0014</td><td>41.5526</td></tr><tr><td>(-3.6775)</td><td>(0.6699)</td></tr></table></td><td><table><tr><td>-1.35E-6</td><td>0.025</td></tr><tr><td>(-2.9539)</td><td>(1.2025)</td></tr><tr><td>-0.0003</td><td>3.5839</td></tr><tr><td>(-1.4134)</td><td>(0.3091)</td></tr><tr><td>-1.08E-5</td><td>-0.0836</td></tr><tr><td>(-1.6067)</td><td>(-0.2731)</td></tr><tr><td>0.0002</td><td>-15.3909</td></tr><tr><td>(1.4943)</td><td>(-1.8527)</td></tr></table></td></tr><tr><th>INDONESIA</th><th>THAILAND</th></tr><tr><td><table><tr><td>6.66E-6</td><td>2.45E-5</td></tr><tr><td>(0.6539)</td><td>(0.6399)</td></tr><tr><td>-0.0007</td><td>-0.0037</td></tr><tr><td>(-0.9546)</td><td>(-1.2728)</td></tr><tr><td>0.0038</td><td>0.1801</td></tr><tr><td>(0.1704)</td><td>(2.0988)</td></tr><tr><td>-0.0001</td><td>-0.0055</td></tr><tr><td>(-0.0723)</td><td>(-0.9648)</td></tr></table></td><td><table><tr><td>0.0003</td><td>-0.4691</td></tr><tr><td>(-0.7636)</td><td>(-1.0086)</td></tr><tr><td>-0.0217</td><td>-9.2240</td></tr><tr><td>(-0.8426)</td><td>(-0.3416)</td></tr><tr><td>0.0025</td><td>-1.2629</td></tr><tr><td>(0.7824)</td><td>(-0.3658)</td></tr><tr><td>-0.0110</td><td>-12.4316</td></tr><tr><td>(-1.3544)</td><td>(-1.4572)</td></tr></table></td></tr></tbody></table>

The results show that for South Korea, Indonesia and Thailand the budgetary deficit appreciates the domestic currency and worsens the current account. This result is significant only for South Korea and consistent with the Marchall-Learner condition. The effect of the fiscal policy on the economic activity and the interest rate differential is mitigate and no significant for all the estimated coefficients.

For Mexico and Korea the effects of monetary policy on the exchange rate seem inconsistent with the international monetary theory but the estimated coefficients is no significant at the 5% level.

For the Asian countries the monetary policy reduces the interest rate differential and it has no significant effect on the exchange rate and the current account.

This result suggests that the budgetary deficit and monetary policy do not play a real role in the Asian crises. The Asian crisis are better described by "second-generation models". The motives for devaluation in the Asian countries need for more expansionary monetary policies or budget deficits.
# 5. Conclusion

In this paper we adopt the approach of common trends model to investigate the importance of domestic permanent shocks and foreign shocks on the exchange rate, the current account, the output and the interest rate differentials. We find that:

> The effects of the domestic permanent real and nominal shocks on the exchange rate and the current account balance are consistent with the international economic theory.

> Foreign shocks do have a more important effect on the fluctuations of the exchange rate and the current account in the short and the medium run than permanent domestic shocks do. These results suggest that the Asian countries have exposed their economy to external shocks and that have made their economy vulnerable to them.

> In the case of Thailand, foreign shocks play a bigger role on the fluctuations of the exchange rate and the current account (an average of 70%) than for the other Asian countries and this explains why speculators bet against Thailand.

> The effects of the budgetary deficits and the money supply in the exchange rate and the current account for the Asian countries are generally no significant, this show that the Asian crises is better described by "second-generation" models.
References

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is integrated of order zero, i.e. $z_t$ is stationary. Finally, suppose (iii) that $x_t$ has a finite order unrestricted VAR (UVAR) representation,

(4) $$A(L)x_t = \rho + \epsilon_t$$

where $A(L) = I_n - \sum_{k=1}^{p} A_K L^k$, $E[\epsilon_t | x_k] = 0$ for $t>k$ with $t \ge 1$, $E[\epsilon_t \epsilon_S'] = \Sigma$ if $t=s$ and $E[\epsilon_t \epsilon_S'] = 0$ otherwise. By rearranging terms, (4) can be written as

(5) $$A^*(L)\Delta x = \rho - A(1)x_{t-1} + \epsilon_t$$

where $\Delta = 1-L$, and $A(1) = I_n - \sum_{k=1}^{p} A_k$. The relation between $A(L)$ and $A^*(L)$ is given by

$$A^*(L) = I_n - \sum_{l=1}^{p-1} A_l L^l \quad \text{where} \quad A_1^* - \sum_{k=l+1}^{p} A_k.$$

Since $x_t$ is integrated of order one, implying that $\Delta x_t$ is stationary, it follows from (5) that $A(1)x_{t-1}$ must also be stationary. Hence, cannot be $A(1)x_{t-1}$ must be a linear function of $\alpha' x_{t-1}$, i.e.,

(6) $$A(1) = \gamma \alpha'$$

where $\gamma$ is an $n \times r$ matrix. Since $\text{rank}[A(1)] = \min[\text{rank}(\gamma), \text{rank}(\alpha)]$, the rank of $A(1)$ cannot exceed $r$, i.e. $A(1)$ is singular. An inversion of the polynomial $A(L)$ must take this fact into account. Using (6) we may rewrite (5) on the so called VEC form,

(7) $$A^*(L)\Delta x = \rho - \gamma z_{t-1} + \epsilon_t.$$

King et al. (1987) suggest that impulse response functions may be derived from inverting an estimated VEC model of this kind.

## 1.2 A restricted VAR representation.

If $x_t$ is cointegrated of order (1,1) with cointegrating rank $r$ and cointegrating matrix $\alpha$, then there exists an RVAR of the form

(8) $$B(L)y_t = \theta + \eta_t$$

where $B(L) = M[A^*(L)M^{-1}D(L) + NL]$ and $y_t = \Delta(L)Mx_t$. The $n \times n$ matrix $M$ is equal to $[S'_k \alpha]'$, where the rows of the $k \times n$ selection matrix $S_k$ are linearly independent of the rows $\alpha'$ and $\text{rank}[S_k] = k$; $k=n-r$. Furthermore, $N=[0_{n,k}\gamma]$ and the matrix polynomials $D(L)$ and $\Delta(L)$ are defined by

$$D(L) = \begin{bmatrix} I_k & 0 \\ 0 & \Delta I_r \end{bmatrix} \qquad \Delta(L) = \begin{bmatrix} \Delta I_k & 0 \\ 0 & I_r \end{bmatrix}.$$
Choosing $S_k = [I_k 0]$, the last $r$ elements of the $n \times 1$ vector $y_t$ are the $r$ stationary linear combinaisons $z_t$, while the first $k$ elements are the first differences of the first $k$ elements of $x_t$, i.e., $y_t = [\Delta x_{1,t} \dots \Delta x_{k,t} \ z_{1,t} \dots z_{r,t}]$. The following restrictions relate the RVAR model and the UVAR model (4):

$$ \theta = M \cdot \rho \qquad \eta_t = M \epsilon_t $$

$$ E[\eta_t \eta_t'] = \Omega = M \Sigma M' \qquad A(L) = \dot{M}^{-1} B(L) \Delta(L) M. $$

Since $\Delta(1)$ has rank $r$, it is clear that $A(1)$ also has rank $r$.

$$ (9) \qquad \Delta x_t = \delta + C(L) \epsilon_t $$

where

$$ C(L) \qquad M^{-1} D(L) B(L)^{-1} M \qquad \delta = C(1) M^{-1} \theta. $$

Thus, once the RVAR representation (8) has been estimated, the moving average representation (9) and, hence, impulse functions can easily be obtained.

In order to construct $y_t$ we need first to know the cointegration rank $r$ and the cointegrating vectors in the matrix $\alpha$.

once $y_t$ has been constructed, $\theta, B(L)$, and $\Omega$ (and, hence, $\delta, C(L)$ and $\Sigma$) can be obtained through estimation of the RVAR model (8).

## 1.3 The common trends representation

To see how the common trends parameteres and the permanent shocks may be identified, substitute recursively in (9) to obtain

$$ (10) \qquad x_t = x_0 + \delta t + C(L)(1 + L + L^2 + \dots + L^t)\epsilon_t, $$

which in turn may be rewritten as

$$ (11) \qquad x_t = x_0 + C(1)\xi_t + C(\tilde{L})\epsilon_t. $$

The vector $\xi_t$ is a random walk with drift according to $\xi_t = \rho + \xi_{t-1} + \epsilon_t$, the constant $\rho$ being defined from $C(1)\rho = \delta$. The relationship between the polynomials $C(\tilde{L})\epsilon_t = -\sum_{j=i+1}^{\infty} C_j$. The long run behavior of $x_t$ will thus be dominated by the non-stationary, random walk component $C(1)\xi_t$, but since $\alpha'x_t$ is stationary it must hold that $\alpha'C(1) = 0$, i.e., $C(1)$ must be of less than full rank. specifically, it has rank n-r.

Combinig (2), (3), and (11), we find that $\delta = A\mu$ and $C(1)\epsilon_t = A\psi_t$. the matrix $A$ and the vector $\mu$ can be estimated as follows. Let the matrix of the innovations to the stockastic trends, $\psi_t$, be denoted by $\Psi$; it follows that

$$ (12) \qquad A\Psi A' = C(1)\Sigma C(1)' $$
if we want to derive the common trends model we have to put enough restrictions on A and Ψ to be able to identify their elements from (12). Given C(1), which has rank k, and Σ, we can uniquely determine k(k + 1)/2 parameters of A and Ψ from (12). If Ψ is assumed to be given by the identity matrix, this implies that k(k + 1)/2 of A can be determined.

We assume that $A = A_0\pi$, where $A_0$ is an $n \times k$ matrix of known parameters, which must satisfy $\alpha' A_0 = 0$. In order for $\alpha' x_t$ to be stationary, and that $\pi$ is lower triangular.

Letting $A = A_0\pi$ we have from (12) that

$$ (13) \qquad \pi'\pi = (A_0'A_0)^{-1}A_0'C(1)\Sigma C(1)'A_0(A_0'A_0)^{-1}. $$

From a choleski decomposition of $\pi'\pi$ we can calculate the following parameters of the common trends model:

$$ A = A_0 \pi $$

$$ \hat{\mu} = (\hat{A}'\hat{A})^{-1}\hat{A}'\hat{C}(1)M^{-1}\hat{\theta} $$

where a circumflex denotes an estimate.
DECOMPOSITION OF THE VARIANCE OF CURRENT ACCOUNT

<table><thead><tr><th>S</th><th>REAL SHOCK</th><th>KOREA<br>NOMINAL SHOCK</th><th>FOREIGN SHOCK</th><th>REAL SHOCK</th><th>INDONESIA<br>NOMINAL SHOCK</th><th>FOREIGN SHOCK</th></tr></thead><tbody><tr><td>1</td><td>0.0012</td><td>0.7195</td><td>0.2791</td><td>0.0012</td><td>0.0833</td><td><strong>0.9152</strong></td></tr><tr><td>2</td><td>0.0008</td><td>0.7723</td><td>0.2267</td><td>0.0018</td><td>0.0777</td><td><strong>0.9203</strong></td></tr><tr><td>3</td><td>0.0031</td><td>0.7911</td><td>0.2056</td><td>0.0222</td><td>0.0861</td><td><strong>0.8915</strong></td></tr><tr><td>4</td><td>0.0098</td><td>0.8047</td><td>0.1854</td><td>0.0502</td><td>0.1124</td><td><strong>0.8372</strong></td></tr><tr><td>10</td><td>0.0168</td><td>0.8302</td><td>0.1528</td><td>0.1559</td><td>0.3520</td><td><strong>0.2921</strong></td></tr><tr><td>15</td><td>0.0169</td><td>0.8245</td><td>0.1584</td><td>0.1046</td><td>0.4750</td><td><strong>0.4202</strong></td></tr><tr><td>20</td><td>0.0167</td><td>0.8237</td><td>0.1594</td><td>0.1140</td><td>0.5451</td><td><strong>0.3407</strong></td></tr><tr><td>∞</td><td>0.0169</td><td>0.9830</td><td>0.0000</td><td>0.1504</td><td>0.8495</td><td><strong>0.0000</strong></td></tr><tr><td></td><td></td><td>MEXICO</td><td></td><td></td><td>THAILAND</td><td></td></tr><tr><td>1</td><td>0.6602</td><td>0.2226</td><td>0.1171</td><td>0.0813</td><td>0.3994</td><td><strong>0.5191</strong></td></tr><tr><td>2</td><td>0.5061</td><td>0.4371</td><td>0.0566</td><td>0.0534</td><td>0.2254</td><td><strong>0.7211</strong></td></tr><tr><td>3</td><td>0.4249</td><td>0.5236</td><td>0.0513</td><td>0.0602</td><td>0.1688</td><td><strong>0.7708</strong></td></tr><tr><td>4</td><td>0.3932</td><td>0.5651</td><td>0.0415</td><td>0.0652</td><td>0.1469</td><td><strong>0.7877</strong></td></tr><tr><td>10</td><td>0.3368</td><td>0.6453</td><td>0.0177</td><td>0.0674</td><td>0.0925</td><td><strong>0.8399</strong></td></tr><tr><td>15</td><td>0.3392</td><td>0.6482</td><td>0.0124</td><td>0.0769</td><td>0.0919</td><td><strong>0.8310</strong></td></tr><tr><td>20</td><td>0.3424</td><td>0.6481</td><td>0.0093</td><td>0.0782</td><td>0.1032</td><td><strong>0.8184</strong></td></tr><tr><td>∞</td><td>0.4301</td><td>0.5698</td><td>0.0000</td><td>0.0053</td><td>0.9946</td><td><strong>0.0000</strong></td></tr></tbody></table>

DECOMPOSITION OF THE VARIANCE OF NOMINAL EXCHANGE RATE

<table><thead><tr><th></th><th>REAL SHOCK</th><th>KOREA<br>NOMINAL SHOCK</th><th>FOREIGN SHOCK</th><th>REAL SHOCK</th><th>INDONESIA<br>NOMINAL SHOCK</th><th>FOREIGN SHOCK</th></tr></thead><tbody><tr><td>1</td><td>0.0007</td><td>0.2922</td><td><strong>0.7070</strong></td><td>0.0314</td><td>0.8570</td><td>0.1114</td></tr><tr><td>2</td><td>0.0726</td><td>0.2922</td><td><strong>0.6350</strong></td><td>0.0155</td><td>0.9194</td><td>0.0649</td></tr><tr><td>3</td><td>0.0631</td><td>0.3661</td><td><strong>0.5706</strong></td><td>0.0159</td><td>0.9327</td><td>0.0512</td></tr><tr><td>4</td><td>0.0648</td><td>0.4491</td><td><strong>0.4858</strong></td><td>0.0138</td><td>0.9392</td><td>0.0468</td></tr><tr><td>10</td><td>0.0850</td><td>0.7272</td><td><strong>0.1807</strong></td><td>0.0690</td><td>0.9740</td><td>0.1505</td></tr><tr><td>15</td><td>0.2539</td><td>0.7996</td><td><strong>0.1039</strong></td><td>0.0050</td><td>0.9815</td><td>0.0133</td></tr><tr><td>20</td><td>0.1012</td><td>0.8300</td><td><strong>0.0686</strong></td><td>0.0043</td><td>0.9855</td><td>0.0100</td></tr><tr><td>∞</td><td>0.1103</td><td>0.8896</td><td><strong>0.0000</strong></td><td>0.0020</td><td>0.9979</td><td>0.0000</td></tr><tr><td></td><td></td><td>MEXICO</td><td></td><td></td><td>THAILAND</td><td></td></tr><tr><td>1</td><td>0.0136</td><td>0.5340</td><td>0.4521</td><td>0.0403</td><td>0.0139</td><td><strong>0.9455</strong></td></tr><tr><td>2</td><td>0.0049</td><td>0.5014</td><td>0.4935</td><td>0.0661</td><td>0.0052</td><td><strong>0.9286</strong></td></tr><tr><td>3</td><td>0.0147</td><td>0.5533</td><td>0.4318</td><td>0.0940</td><td>0.0291</td><td><strong>0.8767</strong></td></tr><tr><td>4</td><td>0.0202</td><td>0.6152</td><td>0.3644</td><td>0.1322</td><td>0.0687</td><td><strong>0.7989</strong></td></tr><tr><td>10</td><td>0.0835</td><td>0.6822</td><td>0.2341</td><td>0.2871</td><td>0.1763</td><td><strong>0.5364</strong></td></tr><tr><td>15</td><td>0.1659</td><td>0.6610</td><td>0.1729</td><td>0.3723</td><td>0.2394</td><td><strong>0.3880</strong></td></tr><tr><td>20</td><td>0.2441</td><td>0.6237</td><td>0.1319</td><td>0.3924</td><td>0.2410</td><td><strong>0.3665</strong></td></tr><tr><td>∞</td><td>0.7474</td><td>0.2525</td><td>0.0000</td><td>0.1308</td><td>0.8691</td><td><strong>0.0000</strong></td></tr></tbody></table>