Testing 1
5
1. Testing structural stability of the regression model On 11/1/2007, Vietnam has officially become member of WTO, which might have effects on exchange rate. We use dummy variable instead of Chow test to test for the existence of change in structure of the model to simplify procedure. The data is divided into two periods: one from year 1993 to 2006 and one from 2007 to 2013. The following addictive dummy model used: ^ EXRATE t = ^ α 1 + ^ α 2 D t + ^ α 3 VNI t + ^ α 4 ln USI t + ^ α 5 M t + ^ α 6 TR GDPt Where D=1 if 2007-2013 D=0 if 1993-2006 From table 1 we have: ^ EXRATE= 14344.16–680.29 D t –117.13 VNI t –1589.66 ln USI t +0.001071 M t +40.62 TR GDPt Testing difference of the intercept H 0 : two intercepts are the same H 1 : two intercepts are different Test-statistics: t= -0.637 (as seen in Table1) Decision rule: Reject H 0 if |t| ¿ t α 2 ,n−k = t 0.05, 15 = 2.131 We have: |t| = 0.637 ¿ 2.131 => do not reject H 0 Therefore, there is not enough evidence to conclude that there is difference in constant term 2. Testing higher order autocorrelation 2.1. Second-order autocorrelation H 0 : no second-order autocorrelation
-
Upload
quyen-hoang -
Category
Documents
-
view
212 -
download
0
description
test
Transcript of Testing 1
1. Testing structural stability of the regression model On 11/1/2007, Vietnam has officially become member of WTO, which might have effects on exchange rate. We use dummy variable instead of Chow test to test for the existence of change in structure of the model to simplify procedure. The data is divided into two periods: one from year 1993 to 2006 and one from 2007 to 2013. The following addictive dummy model used:
Where D=1 if 2007-2013 D=0 if 1993-2006From table 1 we have:= 14344.16680.29 117.131589.66+0.001071+40.62 Testing difference of the intercept : two intercepts are the same: two intercepts are different Test-statistics: t= -0.637 (as seen in Table1) Decision rule: Reject if |t| = = 2.131 We have: |t| = 0.637 2.131 => do not reject Therefore, there is not enough evidence to conclude that there is difference in constant term2. Testing higher order autocorrelation2.1. Second-order autocorrelation : no second-order autocorrelation: second-order autocorrelation exists Test-statistics:BG-statistics = (n-p)* = (21 2)*0.136 = 2.584(we take the value of in Table 2)
Decision rule: Reject if = = 4.6051 We have: BG = 2.584 => do not reject Therefore, there is not enough evidence to conclude that second-order autocorrelation occurs in the model.2.2. Third-order autocorrelation : no third-order autocorrelation: third-order autocorrelation exists Test-statistics:BG-statistics = (n-p)* = (21 3)*0.2572 = 4.6296(we take the value of in Table 3)
Decision rule: Reject if = = 6.2514 We have: BG = 4.6296 => do not reject Therefore, there is not enough evidence to conclude that third-order autocorrelation occurs in the model.Table1. Dummy regression
Dependent Variable: EXRATE
Method: Least Squares
Date: 19/05/15 Time: 22:40
Sample: 1993 2013
Included observations: 21
VariableCoefficientStd. Errort-StatisticProb.
C14344.162995.1334.7891550.0002
DUMMY-680.29641068.275-0.6368180.5338
VNI-117.128151.56810-2.2713280.0383
LOG(USI)-1589.666850.5020-1.8690910.0813
M0.0010710.0004472.3970870.0300
TR_GDP40.6230319.968932.0343120.0600
R-squared0.934423Mean dependent var15578.56
Adjusted R-squared0.912564S.D. dependent var3188.614
S.E. of regression942.8575Akaike info criterion16.77066
Sum squared resid13334705Schwarz criterion17.06910
Log likelihood-170.0920Hannan-Quinn criter.16.83543
F-statistic42.74797Durbin-Watson stat1.243863
Prob(F-statistic)0.000000
Table2. Breusch-Godfrey serial correlation LM Test for second order
Breusch-Godfrey Serial Correlation LM Test:
F-statistic1.104054Prob. F(2,14)0.3587
Obs*R-squared2.860931Prob. Chi-Square(2)0.2392
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 19/05/15 Time: 21:59
Sample: 1993 2013
Included observations: 21
Presample missing value lagged residuals set to zero.
VariableCoefficientStd. Errort-StatisticProb.
C1.5492682717.4370.0005700.9996
VNI6.42593748.887750.1314430.8973
LOG(USI)-185.2020846.4366-0.2188020.8300
M-0.0001250.000459-0.2725310.7892
TR_GDP2.84562018.254860.1558830.8784
RESID(-1)0.3988980.2847291.4009740.1830
RESID(-2)-0.2257440.289724-0.7791720.4488
R-squared0.136235Mean dependent var-2.20E-12
Adjusted R-squared-0.233950S.D. dependent var827.5028
S.E. of regression919.2176Akaike info criterion16.74612
Sum squared resid11829454Schwarz criterion17.09430
Log likelihood-168.8343Hannan-Quinn criter.16.82169
F-statistic0.368018Durbin-Watson stat1.973946
Prob(F-statistic)0.887259
Table3. Breusch-Godfrey serial correlation LM Test for third order
Breusch-Godfrey Serial Correlation LM Test:
F-statistic1.501144Prob. F(3,13)0.2607
Obs*R-squared5.403059Prob. Chi-Square(3)0.1446
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 19/05/15 Time: 22:01
Sample: 1993 2013
Included observations: 21
Presample missing value lagged residuals set to zero.
VariableCoefficientStd. Errort-StatisticProb.
C1315.0312766.2630.4753820.6424
VNI5.84315347.045740.1242020.9031
LOG(USI)-889.1554947.2649-0.9386560.3650
M-0.0004070.000482-0.8436240.4141
TR_GDP4.24883317.592840.2415090.8129
RESID(-1)0.3930430.2740201.4343560.1751
RESID(-2)-0.0619390.300652-0.2060160.8400
RESID(-3)-0.5124750.352064-1.4556290.1692
R-squared0.257289Mean dependent var-2.20E-12
Adjusted R-squared-0.142633S.D. dependent var827.5028
S.E. of regression884.5510Akaike info criterion16.69037
Sum squared resid10171596Schwarz criterion17.08828
Log likelihood-167.2489Hannan-Quinn criter.16.77673
F-statistic0.643348Durbin-Watson stat2.084975
Prob(F-statistic)0.713993
![Year13 Testing[1]](https://static.fdocuments.us/doc/165x107/55cf865b550346484b96da7c/year13-testing1.jpg)
