Based on the following table of results obtained by OLS for a structural time series with level data. Do you think the model is good or is there a suspicion of doing a spurious regression? in case of the latter, indicate based on what it considers to be spurious Dependent Variable: PIB Method: Least Squares (Gauss-Newton / Marquardt steps) Date: 03/02/21 Time: 16:23 Sample: 1 88 Included observations: 88 PIB =C(1)+C(2)*IPD Coefficient Std. Error t-Statistic Prob. C(1) C(2) 177.5169 51.66695 3.435793 0.0009 1.318508 0.018218 72.37426 0.0000 Mean dependent var S.D. dependent var R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.983847 0.983659 80.37884 Akaike info criterion 3865.094 628.7855 11.63384 11.69015 11.65653 0.815770 555625.2 Schwarz criterion -509.8891 Hannan-Quinn criter. 5238.034 Durbin-Watson stat 0.000000
Based on the following table of results obtained by OLS for a structural time series with level data. Do you think the model is good or is there a suspicion of doing a spurious regression? in case of the latter, indicate based on what it considers to be spurious Dependent Variable: PIB Method: Least Squares (Gauss-Newton / Marquardt steps) Date: 03/02/21 Time: 16:23 Sample: 1 88 Included observations: 88 PIB =C(1)+C(2)*IPD Coefficient Std. Error t-Statistic Prob. C(1) C(2) 177.5169 51.66695 3.435793 0.0009 1.318508 0.018218 72.37426 0.0000 Mean dependent var S.D. dependent var R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.983847 0.983659 80.37884 Akaike info criterion 3865.094 628.7855 11.63384 11.69015 11.65653 0.815770 555625.2 Schwarz criterion -509.8891 Hannan-Quinn criter. 5238.034 Durbin-Watson stat 0.000000
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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Transcribed Image Text:Based on the following table of results obtained by OLS for a structural time series with
level data. Do you think the model is good or is there a suspicion of doing a spurious
regression? in case of the latter, indicate based on what it considers to be spurious
Dependent Variable: PIB
Method: Least Squares (Gauss-Newton / Marquardt steps)
Date: 03/02/21 Time: 16:23
Sample: 1 88
Included observations: 88
PIB =C(1)+C(2)*IPD
Coefficient
Std. Error
t-Statistic
Prob.
C(1)
C(2)
177.5169
51.66695
3.435793
0.0009
1.318508
0.018218
72.37426
0.0000
Mean dependent var
S.D. dependent var
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.983847
0.983659
80.37884 Akaike info criterion
3865.094
628.7855
11.63384
11.69015
11.65653
0.815770
555625.2 Schwarz criterion
-509.8891
Hannan-Quinn criter.
5238.034 Durbin-Watson stat
0.000000
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