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EViews output below shows the results from the Breusch-Godfrey test for autocorrelation in residuals at lag two. Which of the statements below is incorrect: Breusch-Godfrey Serial Correlation LM Test: F-statistic Obs*R-squared Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1990M01 2009M12 Included observations: 240 Presample missing value lagged residuals set to zero. Coefficient Std. Error Variable с EMKT SMB HML RESID(-1) RESID(-2) 0.771667 Prob. F(2,234) 1.572536 Prob. Chi-Square(2) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.4634 0.4555 t-Statistic Prob. -0.003168 0.177289 -0.017868 0.9858 -0.003699 0.041220 -0.089749 0.9286 0.012499 0.054411 0.229706 0.8185 0.003962 0.057368 0.069070 0.9450 -0.040356 0.066184 -0.609749 0.5426 0.070667 0.066513 1.062458 0.2891 0.006552 Mean dependent var -0.014675 S.D. dependent var 2.702019 Akaike info criterion 1708.412 Schwarz criterion -576.0669 Hannan-Quinn criter. 0.308667 Durbin-Watson stat 0.907453 -5.44E-16 2.682408 4.850557 4.937573 4.885619 2.000978 A. The results indicate no serial correlation in residuals only up to lag two. There might be serial correlation present at higher lags. ⒸB. The null hypothesis of no serial autocorrelation up to order 2 cannot be rejected by the LM test. ⒸC. The result from the LM test shows that the hypothesis of no autocorrelation should be rejected. D. The null hypothesis of no serial autocorrelation cannot be rejected by the LM test.

EViews output below shows the results from the Breusch-Godfrey test for autocorrelation in residuals at lag two. Which of the statements below is incorrect: Breusch-Godfrey Serial Correlation LM Test: F-statistic Obs*R-squared Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1990M01 2009M12 Included observations: 240 Presample missing value lagged residuals set to zero. Coefficient Std. Error Variable с EMKT SMB HML RESID(-1) RESID(-2) 0.771667 Prob. F(2,234) 1.572536 Prob. Chi-Square(2) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.4634 0.4555 t-Statistic Prob. -0.003168 0.177289 -0.017868 0.9858 -0.003699 0.041220 -0.089749 0.9286 0.012499 0.054411 0.229706 0.8185 0.003962 0.057368 0.069070 0.9450 -0.040356 0.066184 -0.609749 0.5426 0.070667 0.066513 1.062458 0.2891 0.006552 Mean dependent var -0.014675 S.D. dependent var 2.702019 Akaike info criterion 1708.412 Schwarz criterion -576.0669 Hannan-Quinn criter. 0.308667 Durbin-Watson stat 0.907453 -5.44E-16 2.682408 4.850557 4.937573 4.885619 2.000978 A. The results indicate no serial correlation in residuals only up to lag two. There might be serial correlation present at higher lags. ⒸB. The null hypothesis of no serial autocorrelation up to order 2 cannot be rejected by the LM test. ⒸC. The result from the LM test shows that the hypothesis of no autocorrelation should be rejected. D. The null hypothesis of no serial autocorrelation cannot be rejected by the LM test.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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EViews output below shows the results from the Breusch-Godfrey test for autocorrelation in residuals at lag two. Which of the statements below is incorrect: Breusch-Godfrey Serial Correlation LM Test: F-statistic Obs*R-squared Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1990M01 2009M12 Included observations: 240 Presample missing value lagged residuals set to zero. Coefficient Std. Error Variable с EMKT SMB HML RESID(-1) RESID(-2) 0.771667 Prob. F(2,234) 1.572536 Prob. Chi-Square(2) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.012499 0.054411 0.229706 0.003962 0.057368 0.069070 t-Statistic 0.9858 -0.003168 0.177289 -0.017868 -0.003699 0.041220 -0.089749 0.9286 -0.040356 0.070667 0.4634 0.4555 Prob. 0.006552 Mean dependent var -0.014675 S.D. dependent var 2.702019 Akaike info criterion 1708.412 Schwarz criterion -576.0669 Hannan-Quinn criter. 0.308667 Durbin-Watson stat 0.907453 0.8185 0.9450 0.066184 -0.609749 0.5426 0.066513 1.062458 0.2891 -5.44E-16 2.682408 4.850557 4.937573 4.885619 2.000978 ⒸA. The results indicate no serial correlation in residuals only up to lag two. There might be serial correlation present at higher lags. ⒸB. The null hypothesis of no serial autocorrelation up to order 2 cannot be rejected by the LM test. OC. The result from the LM test shows that the hypothesis of no autocorrelation should be rejected. ⒸD. The null hypothesis of no serial autocorrelation cannot be rejected by the LM test.
Transcribed Image Text:EViews output below shows the results from the Breusch-Godfrey test for autocorrelation in residuals at lag two. Which of the statements below is incorrect: Breusch-Godfrey Serial Correlation LM Test: F-statistic Obs*R-squared Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1990M01 2009M12 Included observations: 240 Presample missing value lagged residuals set to zero. Coefficient Std. Error Variable с EMKT SMB HML RESID(-1) RESID(-2) 0.771667 Prob. F(2,234) 1.572536 Prob. Chi-Square(2) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.012499 0.054411 0.229706 0.003962 0.057368 0.069070 t-Statistic 0.9858 -0.003168 0.177289 -0.017868 -0.003699 0.041220 -0.089749 0.9286 -0.040356 0.070667 0.4634 0.4555 Prob. 0.006552 Mean dependent var -0.014675 S.D. dependent var 2.702019 Akaike info criterion 1708.412 Schwarz criterion -576.0669 Hannan-Quinn criter. 0.308667 Durbin-Watson stat 0.907453 0.8185 0.9450 0.066184 -0.609749 0.5426 0.066513 1.062458 0.2891 -5.44E-16 2.682408 4.850557 4.937573 4.885619 2.000978 ⒸA. The results indicate no serial correlation in residuals only up to lag two. There might be serial correlation present at higher lags. ⒸB. The null hypothesis of no serial autocorrelation up to order 2 cannot be rejected by the LM test. OC. The result from the LM test shows that the hypothesis of no autocorrelation should be rejected. ⒸD. The null hypothesis of no serial autocorrelation cannot be rejected by the LM test.
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