Silviase/jgov_v1.4_full_ann huggingface dataset:
#### A.2.8 Conclusion

In the last table, target of the exchange rate policy measured by reaction function and result of shock on the exchange rate measured by VAR are summarized. As we have discussed in the case of the monetary policy, there are identification problems that we must consider. Apart from these econometric problems, we found it difficult to interpret the results summarized in the Table A2.8-1, because the results of the reaction function and VAR are different, and we found hardly any interrelation between the two analyses. One other shortcoming of this analysis is that we did not attempt a similar exercise with the money demand function in order to examine the effectiveness of the monetary policy.

However, even with these reservations, the table shows many interesting findings. In the Japanese case, the results of the reaction function show that the target of the foreign exchange rate policy was the current account balance. But the results we obtained from the VAR analysis show that the exchange rate was an independent variable, and its fluctuation was not severely effected by the fluctuation of other variables. But on the other hand, effects of the fluctuation of the exchange rate had impact on other variables. It depends on the variables included in the VAR equations, however, the fluctuations of the exchange rate do not very much affect the current account, but do have an impact on the growth of the real GDP. Also we can observe the impact on the gross international reserves as well as the inflation. What confuses us is that in the reaction function, it has significance on current accounts balance while the in VAR, we obtained quite different results.

The results of Korea were not as we have expected. We expected significant effects from the Probit analysis in the 1970's, but the result was that we obtained the result from Maximum likelihood estimation, which were the effects on industrial production index and current account balance. And from the VAR analysis, the indication of the results was that the exchange rate had some impact on the shock of real GDP growth and current account balance. The estimation of the 1980's seems to have reasonable results, wherein the reaction function shows that there was a policy response by the exchange rate to the real GDP growth and current account, and the VAR proved the impact of the exchange rate shock on these two variables. When we took the 1970's and 1980's as an estimation period, the results seem to be convincing. In the reaction function, exchange rate responds to the real GDP growth, inflation, industrial production index, and the current account balance. The result of the VAR show that the shock of the exchange rate had a significant impact on real GDP growth, current accounts balance and inflation.
The results of the reaction function on Indonesia indicate that the target of the exchange rate policy was current account balance and wholesale price inflation from the maximum likelihood analysis, and change of reserves from Probit estimation. From the VAR, we found that the exchange rate had an impact on current account balance and real GDP growth rate, when the oil price is not included in the equation. When the oil price is included, it dominates the effect of the other variables particularly in the 1980s. This result coincides with the implication mentioned in Komatsu (1993), that the Indonesian authorities were less concerned with inflation.

The result of the Philippines were quite different from the other counties. From the results of the reaction function, targets of the exchange rate policy were real GDP growth, and inflation from the maximum likelihood method, and inflation and current accounts balance from the probit method. On the other hand, it seems that the exchange rate was not an independent variable, but it was effected by the other variables.

Let us summarize the results of our study in the light of the initial question we raised at the beginning of the study.

What are the targets of the exchange rate policy? This question can be answered by the results of the reaction function analysis. In the case of Japan, current account can be considered as a target of the exchange rate policy, if the reaction function analysis is an appropriate instrument to apply. The results of the Korean case show that real GDP growth, industrial production, and current account balance was the target. In Indonesia, current account balance and inflation were the target. While in the case of the Philippines, inflation and current account were the targets.

Does the exchange rate policy focuses on adjusting current account balance? For this question, the answer for Japan is yes and no. The result of the reaction function show that Japanese authorities focused on adjusting current accounts balance as a target of exchange rate policy. But as we have explained in the first place, this reaction function analysis does not fit the analysis of the exchange rate policy of Japan. For the case of Korea, we can clearly interpret that the target of the exchange rate policy was focused on current account balance. Also for the case of Indonesia, one of the important target of the exchange rate policy was current account balance. But in the case of the Philippines, we could not find apparent evidence that the current account balance was the target of the exchange rate policy.

What were the results of the exchange rate policy? This question can be answered by the results of the VAR analyses. In the case of Japan, shock on exchange rate affected real GNP growth rather than the current account balance. In Korea, we can see that shock on the exchange rate had impact on economic growth and current account balance. We can explicate that in the case of Korea, the exchange rate had a
significant impact on the macroeconomic variables, and can be considered to be successful case of the exchange rate policy. The Indonesian case is difficult to interpret, because of the strong effect of oil price. However, results of the VAR analysis show signs of successful adjustment. The exchange rate adjustment did not seem to have significant impact on macroeconomic variables in the Philippines.

The result of this analysis suggests that Korea was successful in utilizing exchange rate as a policy tool while the Philippines seemed to be unsuccessful on the exchange rate policy. This implication seems to coincide with the overall macroeconomic performance.

Regarding the exchange rate policy, which has been one of the central issues of international economics, with many studies have been written on developed and developing countries. However in this study, several important issues of the exchange rate policy are not included. First, no special issues of the exchange rate regime are raised. We have applied Maximum Likelihood estimation when the exchange rate can be considered as a flexible exchange rate regime, and Probit estimation when it can be considered to be a fixed exchange rate regime, although authorities adjust the exchange rate occasionally. Second, openness of the economy, the larger degree of exports and imports in the economy is not particularly considered. Third, the effect of the capital mobility in not also considered when applying the analysis. $ ^{9} $

Another important issue is the existence of the black or parallel market which plays an important role in a country where foreign exchange transactions and trade are not fully liberalized. The importance of the black market is explained in the series of studies by Edwards (1988, 1989). However, since we are analyzing the policy response by the authorities, here we will not discuss the issue of the black market. $ ^{10} $

In van Wijinbergen (1986), the importance of distinguishing between real and nominal exchange rates has been pointed out. This is because real (effective) exchange rate is a relative price of tradable goods with respect to non-tradable goods, but nominal exchange rate is a monetary concept.
However, despite these important distinctions, we have only dealt with the nominal exchange rate because authorities observe and control them.

For the future study, we found several important issues on the exchange rate analysis that we must tackle. First, we started this analysis as we have done in the monetary policy case, we only focused on the nominal exchange rate, because it is the only instrument that the authority can control. However among other studies of the exchange rate, many focus on the importance of the real exchange rate. We might find more interesting facts if we produced reaction function of real exchange rate rather than including inflation rate in the explanatory variable. Second problem is to apply the Probit estimation in the fixed exchange rate case. In Edwards (1989), Probit estimation was applied to the pooled data of 17 counties, because there were not enough incidents of the substantial and discrete devaluation in each country. $ ^{11} $ The explanatory variables used in this estimation were: international liquidity and its ratio to net foreign assets, domestic credit, real exchange rate and parallel market premium. The results of this study confirmed that worsening of international reserves, real appreciation of the exchange rate, expansive fiscal policies and high parallel market premiums increase the probability of the devaluation. However, in this study as in ours, the problem of measuring the magnitude of the devaluation is not solved. As we have discussed earlier, dependant variables of the Probit analysis only take the numbers of 0 or 1. The problem here is that if the measurement of the devaluation is taken as same, that is 1, how large is the devaluation? The method and model used in Asako and Kano (1989) applying Baysean estimation method can be applied as an interesting reference when expanding this study.
Table A2.8-1	Analysis of the Foreign Exchange Rate Policy
Period	Type	Reaction Function	VAR
Jpan	1973:2-1990:4	ML	JBP	JEX->JR->JPY(1-2)
JEX->JPW(1-3)
JPC<->JPY(1-4)
JEX->JPW->JR, JEX->JR(1-5)
JEX->JR->JPC<->JPY(1-6)
Korea	1970:2-1980:4	Probit	None
ML	KRIIP, KRBP
1981:1-1990:4	ML	KRY, KRR, KRBP	KREX->KRY, KREX->KRBP(2-3)
KREX->KRY, KREX->KRBP(2-5,2-6)
KREX->KRPW(2-7)
1970:2-1990:4	ML	KRY, KRPW, KRPC, KRIIP, KR, KRBP
Indonesia	1976:12-1991:4	ML	IPW, IBP	IEX->IY7, IBP->IY7(3-1,I)
IEX->IY8(3-2,II)
IEX->MR, EEX->IY7, IEX->IBP(3-3,I)
OP->IY7, OP->IBP, IEX->IBP(3-4,II)
IMR->IY8, IBP->IY8(3-5,II)
OP->IEX, OP->IBP, OP->IY8(3-6,II)
IEX->IPW->IBP, IEX->IBP, IPW->IY7(3-7,I)
IEX->IPW->IBP, IEX->IY8(3-8,II)
Philippines	1981:2-1991:4	ML	PHY, PHPW, PHPCP	PHBP->PHEX(4-1)
PHTBR->IEX, PHTBR->PHBP(4-2)
PHLR->PHEX, PHLR<->PHBP(4-3)
PHEX->PHPPC->PHBP(4-4)
PHEX<->PHPW(4-5)
Probit

(Note) 1. The numbers in the parenthesis indicate the equations. In the Indonesian case, I is for period I(1976:3-1983:4) and II is for period II(1983:3-1989:4).

2. For the definition of variables, see Chapter4.
Dependent variables: JEX JBP JPY (Equation 1-1)

Current sample: 1973:1 to 1990:4

Number of observations: 72

Schwarz Bayes. Info. Crit. = 1.00372

Log of likelihood function = -316.965

Variable	Estimated	Standard
Coefficient	Error	t-statistic
JEXEQ	JEX (-1)	.145300	.117356	1.23811
JBP (-1)	-.460132E-05	.255250E-05	-1.80267
JPY (-1)	-.387788	.825498	-.469792
C	-.371626E-02	.010966	-.338887
JBPEQ	JEX (-1)	-5237.23	5240.28	-.999419
JBP (-1)	-.244940	.113976	-2.14905
JPY (-1)	-79949.7	36860.7	-2.16897
C	875.667	489.665	178830
JPYEQ	JEX (-1)	-.694728E-02	.016741	-.414982
JBP (-1)	-.344672E-06	.364120E-06	-.946588
JPY (-1)	.028644	.117759	.243244
C	.971342E-02	.156434E-02	6.20929

Durbin-Watson statistic = 1.95716

F-stat. (block exogeneity) = 1.79937

Dependent variable: JBP

Std. error of regression = 2621.98

Durbin-Watson statistic = 2.07330

F-stat. (block exogeneity) = 2.74252

Dependent variable: JPY

Std. error of regression = .837646E-02

Durbin-Watson statistic = 2.06305

F-stat. (block exogeneity) = .497027

Forecast error variance decomposition

JEX	JBP	JPY
JEX	95.88433	3.83493	0.28074
$ \rightarrow $ 95.61281	$ \rightarrow $ 3.93127	$ \rightarrow $ 0.45592
JBP	3.45737	90.81567	5.72696
$ \rightarrow $ 3.40151	$ \rightarrow $ 90.55920	$ \rightarrow $ 6.03929
JPY	0.85055	1.20273	97.94672
$ \rightarrow $ 0.85125	$ \rightarrow $ 1.33029	$ \rightarrow $ 97.81846
Dependent variables: JMR JEX JBP JPY (Equation 1 -2)

Current sample: 1973:1 to 1990:4

Number of observations: 72

Schwarz Bayes. Info. Crit. = .971052

Log of likelihood function = -400.846

JMREQ	Variable	Estimated Coefficient	Standard Error	t-statistic
JMR (-1)	.924738	.045615	20.2727
JEX (-1)	4.62094	1.81265	2.54928
JBP (-1)	-.171286E-04	.395547E-04	-.433036
JPY (-1)	3.29491	13.7341	.239907
C	.560888	.397968	1.40938
JEXEQ	JMR (-1)	.174584E-02	.297657E-02	.586527
JEX (-1)	.139918	.118283	1.18291
JBP (-1)	-.477072E-05	.258111E-05	-1.84832
JPY (-1)	-.188794	.896210	-.210659
C	-.017509	.025969	-.674209
JBPEQ	JMR (-1)	105.272	132.630	.793724
JEX (-1)	-5561.75	5270.47	-1.05527
JBP (-1)	-.255154	.115010	-2.21855
JPY (-1)	-67950.7	39933.4	-1.70160
C	44.0056	1157.13	.038030
JPYEQ	JMR (-1)	-.100888E-02	.407469E-03	-2.47598
JEX (-1)	-.383727E-02	.016192	-.236985
JBP (-1)	-.246780E-06	.353334E-06	-.698431
JPY (-1)	-.086350	.122684	-.703839
C	.017684	.355497E-02	4.97437

Dependent variable: JMR

Std. error of regression = .904229

Durbin-Watson statistic = 1.03151

F-stat. (block exogeneity) = 2.35142

Dependent variable: JEX

Std. error of regression = .059005

Durbin-Watson statistic = 1.95518

F-stat. (block exogeneity) = 1.30268

Dependent variable: JBP
Std. error of regression = 2629.14
Durbin-Watson statistic = 2.07787
F-stat. (block exogeneity) = 2.02840

Forecast error variance decomposition

JMR	JEX	JBP	JPY
JMR	94.89751	4.94520	0.11655	0.04073
$ \rightarrow $ 86.76233	$ \rightarrow $ 12.31265	$ \rightarrow $ 0.81502	$ \rightarrow $ 0.11000
JEX	0.58100	95.50431	3.85855	0.05614
$ \rightarrow $ 0.82879	$ \rightarrow $ 95.01136	$ \rightarrow $ 3.93302	$ \rightarrow $ 0.22683
JBP	5.17464	4.09113	87.21561	3.51862
$ \rightarrow $ 5.96351	$ \rightarrow $ 4.12193	$ \rightarrow $ 85.96476	$ \rightarrow $ 3.94980
JPY	8.75335	0.65416	0.61005	89.98243
$ \rightarrow $ 14.18627	$ \rightarrow $ 1.60523	$ \rightarrow $ 0.75543	$ \rightarrow $ 83.45307
Dependent variables: JEX JPW JBP JPY (Equation 1-3)

Current sample: 1973:1 to 1990:4

Number of observations: 72

Schwarz Bayes. Info. Crit. = -19.1141

Log of likelihood function = 322.220

JEXEQ	Variable	Estimated Coefficient	Standard Error	t-statistic
JEX (-1)	.165285	.124436	1.32827
JPW (-1)	.028254	.285615	.098922
JBP (-1)	-.316160E-04	.123420E-02	-.025617
JPY (-1)	-.464020	.887868	-.522623
C	-.338345E-02	.012558	-.269426
JPWEQ	JEX (-1)	.110651	.033633	3.28991
JPW (-1)	.737248	.077198	9.55009
JBP (-1)	-.291099E-03	.333587E-03	-.872633
JPY (-1)	.651592	.239979	2.71521
C	-.394692E-02	.339426E-02	-1.16282
JBPEQ	JEX (-1)	-6.73761	12.3682	-.544751
JPW (-1)	16.2456	28.3885	.572261
JBP (-1)	.012497	.122672	.101876
JPY (-1)	24.6697	88.2488	.279547
C	-1.46740	1.24819	-1.17563
JPYEQ	JEX (-1)	.439456E-02	.016836	.261016
JPW (-1)	-.079089	.038644	-2.04661
JBP (-1)	.169256E-03	.166988E-03	1.01358
JPY (-1)	-.036954	.120129	-.307617
C	.011356	.169911E-02	6.68330

Dependent variable: JEX

Std. error of regression = .060548

Durbin-Watson statistic = 1.93327

F-stat. (block exogeneity) = .112858

Dependent variable: JPW

Std. error of regression = .016365

Durbin-Watson statistic = 1.79516

F-stat. (block exogeneity) = 6.72355

Dependent variable: JBP

Std. error of regression = 6.01816

Durbin-Watson statistic = 1.99198

F-stat. (block exogeneity) = .175243

Dependent variable: JPY

Std. error of regression = .819227E-02

Durbin-Watson statistic = 2.07517

F-stat. (block exogeneity) = 1.71048

Forecast error variance decomposition

JEX	JPW	JBP	JPY
JEX	99.59703	0.045595	0.00030650	0.35707
$ \rightarrow $ 99.53310	$ \rightarrow $ 0.095993	$ \rightarrow $ 0.0087214	$ \rightarrow $ 0.36218
JPW	24.20128	69.56178	0.92979	5.30715
$ \rightarrow $ 34.75571	$ \rightarrow $ 58.51410	$ \rightarrow $ 0.84435	$ \rightarrow $ 5.88585
JBP	0.76281	0.52362	98.60858	0.10499
$ \rightarrow $ 0.84090	$ \rightarrow $ 0.61062	$ \rightarrow $ 98.40504	$ \rightarrow $ 0.14344
JPY	0.37826	6.97163	2.14013	90.50998
$ \rightarrow $ 2.24438	$ \rightarrow $ 8.07049	$ \rightarrow $ 2.10304	$ \rightarrow $ 87.58209
Dependent variables: JEX JPC JBP JPY (Equation 1-4)

Current sample: 1973:1 to 1990:4

Number of observations: 72

Schwarz Bayes. Info. Crit. = -20.1996

Log of likelihood function = 361.296

JEXEQ	Variable	Estimated Coefficient	Standard Error	t-statistic
JEX (-1)	.168003	.120624	1.39277
JPC (-1)	.117445	.525093	.223664
JBP (-1)	.194377E-05	.123863E-02	.156929E-02
JPY (-1)	-.395452	.948620	-.416871
C	-.534489E-02	.015903	-.336094
JPCEQ	JEX (-1)	.044618	.019355	2.30517
JPC (-1)	.826146	.084257	9.80510
JBP (-1)	-.180527E-03	.198751E-03	-.908308
JPY (-1)	.473330	.152216	3.10959
C	-.231935E-02	.255180E-02	-.908909
JBPEQ	EXR (-1)	-4.91531	11.9795	-.410309
JPC (-1)	-36.0703	52.1484	-.691686
JBP (-1)	.734289E-02	.123012	.059693
JPY (-1)	-17.8579	94.2100	-.189555
C	-.388381	1.57937	-.245909
JPYEQ	JEX (-1)	-.366049E-02	.016292	-.224677
JPC (-1)	-.150113	.070922	-2.11660
JBP (-1)	.117476E-03	.167296E-03	.702205
JPY (-1)	-.087574	.128126	-.683502
C	.013041	.214794E-02	6.07132

Dependent variable: JEX

Std. error of regression = .060530

Durbin-Watson statistic = 1.93795

F-stat. (block exogeneity) = .126337

Dependent variable: JPC

Std. error of regression = .971272E-02

Durbin-Watson statistic = 2.28777

F-stat. (block exogeneity) = 5.45779

Forecast error variance decomposition

JEX	JPC	JBP	JPY
JEX	99.61314	0.15438	0.0040379	0.22844
$ \rightarrow $ 99.42544	$ \rightarrow $ 0.31456	$ \rightarrow $ 0.012136	$ \rightarrow $ 0.24786
JPC	3.50891	87.04447	1.51186	7.93476
$ \rightarrow $ 7.89456	$ \rightarrow $ 79.40627	$ \rightarrow $ 1.74317	$ \rightarrow $ 10.95601
JBP	0.60997	0.37359	98.96789	0.048544
$ \rightarrow $ 0.70483	$ \rightarrow $ 0.70405	$ \rightarrow $ 98.46874	$ \rightarrow $ 0.12238
JPY	0.34907	16.95615	2.22616	80.46863
$ \rightarrow $ 0.94874	$ \rightarrow $ 18.99431	$ \rightarrow $ 2.22465	$ \rightarrow $ 77.83230
Dependant variables: JEX JPW JMR JPY (Equation1-5)

Current sample: 1973:1 to 1990:4

Number of observations: 72

Variable	Estimated Coefficient	Standard Error	t-statistic
JEXEQ	JEX (-1)	.163701	.124404	1.31588
JPW (-1)	.010917	.301868	.036164
JMR (-1)	.816994E-03	.475391E-02	.171857
JPY (-1)	-.440374	.894174	-.492492
C	-.924564E-02	.036448	-.253666
JPWEQ	JEX (-1)	.114965	.033734	3.40794
JPW (-1)	.749906	.081857	9.16121
JMR (-1)	-.763804E-03	.128910E-02	-.592508
JPY (-1)	.647220	.242471	2.66927
C	.173453E-02	.988352E-02	.175498
JMREQ	JEX (-1)	2.56593	.920974	2.78611
JPW (-1)	7.04782	2.23475	3.15374
JMR (-1)	.895345	.035194	25.4406
JPY (-1)	3.45457	6.61964	.521867
C	.679229	.269828	2.51727
JPYEQ	JEX (-1)	.508994E-02	.016684	.305079
JPW (-1)	-.057245	.040484	-1.41403
JMR (-1)	-.960473E-03	.637553E-03	-1.50650
JPY (-1)	-.072058	.119919	-.600887
C	.018165	.488810E-02	3.71609

Dependent variable: JEX

Std. error of regression = .060535

Durbin-Watson statistic = 1.93203

F-stat. (block exogeneity) = .122533

Dependent variable: JPW

Std. error of regression = .016415

Durbin-Watson statistic = 1.81585

F-stat. (block exogeneity) = 6.54756

Dependent variable: JPY

Std. error of regression = .811848E-02

Durbin-Watson statistic = 2.06247

F-stat. (block exogeneity) = 2.14953

Forecast error variance decomposition

JEX	JPW	JMR	JPY
JEX	99.65554	0.039220	0.0026187	0.30262
$ \rightarrow $ 99.53343	$ \rightarrow $ 0.12635	$ \rightarrow $ 0.032425	$ \rightarrow $ 0.30780
JPW	24.76385	70.23622	0.080560	4.91937
$ \rightarrow $ 33.99333	$ \rightarrow $ 59.63263	$ \rightarrow $ 0.93650	$ \rightarrow $ 5.43755
JMR	27.36313	13.54036	58.95259	0.14392
$ \rightarrow $ 48.11623	$ \rightarrow $ 25.23046	$ \rightarrow $ 25.17665	$ \rightarrow $ 1.47666
JPY	0.32578	7.89642	3.49162	88.28617
$ \rightarrow $ 4.03493	$ \rightarrow $ 9.72177	$ \rightarrow $ 3.74179	$ \rightarrow $ 82.50151
Dependent variables: JEX JPC JMR JPY (Equation 1-6)

Current sample: 1973:1 to 1990:4

Number of observations: 72

Variable	Estimated Coefficient	Standard Error	t-statistic
JEXEQ	JEX (-1)	.165613	.120452	1.37493
JPC (-1)	.011995	.630625	.019020
JMR (-1)	.109115E-02	.366256E-02	.297921
JPY (-1)	-.355282	.949926	-.374010
C	-.011689	.026567	-.439985
JPCEQ	JEX (-1)	.044906	.019360	2.31953
JPC (-1)	.786622	.101359	7.76073
JMR (-1)	.488593E-03	.588677E-03	.829984
JPY (-1)	.509074	.152680	3.33426
C	-.524933E-02	.427005E-02	-1.22934
JMREQ	JEX (-1)	4.85563	1.61302	3.01027
JPC (-1)	34.5300	8.44498	4.08882
JMR (-1)	.810056	.049047	16.5160
JPY (-1)	16.8798	12.7209	1.32694
C	.729115	.355768	2.04941
JPYEQ	JEX (-1)	-.283967E-02	.016035	-.177091
JPC (-1)	-.079651	.083952	-.948773
JMR (-1)	-.781287E-03	.487576E-03	-1.60239
JPY (-1)	-.127973	.126459	-1.01198
C	.017642	.353670E-02	4.98822

Dependent variable: JEX

Std. error of regression = .060490

Durbin-Watson statistic = 1.92925

F-stat. (block exogeneity) = .156090

Dependent variable: JPC

Std. error of regression = .972249E-02

Durbin-Watson statistic = 2.21096

F-stat. (block exogeneity) = 5.40201

Dependent variable: JMR

Forecast error variance decomposition

JEX	JPC	JMR	JPY
JEX	99.71831	0.064449	0.037405	0.17983
$ \rightarrow $ 99.26309	$ \rightarrow $ 0.39469	$ \rightarrow $ 0.11784	$ \rightarrow $ 0.22439
JPC	3.41220	87.40096	0.016394	9.17045
$ \rightarrow $ 8.27384	$ \rightarrow $ 79.91480	$ \rightarrow $ 0.076367	$ \rightarrow $ 11.73499
JMR	4.85668	26.73472	67.28500	1.12360
$ \rightarrow $ 14.11605	$ \rightarrow $ 54.68523	$ \rightarrow $ 23.32278	$ \rightarrow $ 7.87594
JPY	0.24836	15.45154	1.92030	82.37979
$ \rightarrow $ 1.59383	$ \rightarrow $ 19.51487	$ \rightarrow $ 2.43827	$ \rightarrow $ 76.45302
## V ector Auto Regression (Korea)

*****

Dependent variables: KREX KRBP KRY (Equation 2-1)

Current sample: 1970:3 to 1990:4

Number of observations: 82

Schwarz Bayes. Info. Crit. = -.076629

Log of likelihood function = -319.477

Variable	Estimated	Standard
Coefficient	Error	t-statistic
KREXEQ	KREX (-1)	.094031	.115218	.816110
KRBP (-1)	-.363077E-05	.680270E-05	-.533725
KRY (-1)	-.167608	.164829	-1.01686
C	.013542	.598131E-02	2.26403
KRBPEQ	KREX (-1)	1097.16	2027.13	.541239
KRBP (-1)	-.252205	.119686	-2.10722
KRY (-1)	389.054	2899.98	.134158
C	-34.4954	105.234	-.327796
KRYEQ	KREX (-1)	-.071711	.079641	-.900425
KRBP (-1)	.123746E-05	.470216E-05	.263168
KRY (-1)	-.234098	.113933	-2.05469
C	.026639	.413441E-02	6.44318

Dependent variable: KREX

Std. error of regression = .040056

Durbin-Watson statistic = 2.08440

F-stat. (block exogeneity) = .619661

Dependent variable: KRBP

Std. error of regression = 704.736

Durbin-Watson statistic = 2.01891

F-stat. (block exogeneity) = .146493

Dependent variable: KRY

Std. error of regression = .027687

Durbin-Watson statistic = 1.97897

F-stat. (block exogeneity) = .472664

Forecast error variance decomposition

KREX	KRBP	KRY
KREX	98.41964	0.36544	1.21492
$ \rightarrow $ 98.39237	$ \rightarrow $ 0.36938	$ \rightarrow $ 1.23825
KRBP	1.77629	98.20330	0.020412
$ \rightarrow $ 1.78988	$ \rightarrow $ 98.16788	$ \rightarrow $ 0.042235
KRY	6.31879	0.14909	93.53212
$ \rightarrow $ 6.29126	$ \rightarrow $ 0.15888	$ \rightarrow $ 93.54986