Homework Help is Here – Start Your Trial Now!
Math
Statistics
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
See similar textbooks
icon
Related questions
Question
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.
Expert Solution
steps

Step by step

Solved in 3 steps with 5 images

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Similar questions
  • SEE MORE QUESTIONS
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman
SEE MORE TEXTBOOKS