An AR(2) model is fitted to time series INTEREST (interest rate). The least-squares estimation results and the residual correlogram are given above. Which statement is correct about the results? Select one: O a. The AR(2) model is not suitable, due to the evidence of autocorrelated error term The AR(2) model is suitable, due to little evidence of autocorrelated error term O c. The AR (2) model is not suitable, due to the evidence of heteroskedasticity O b.

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Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris
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Dependent Variable: INTEREST
Method: Least Squares
Date: 09/10/20 Time: 21:08
Sample (adjusted): 2010M03 2019M12
Included observations: 118 after adjustments
Coefficient
-0.009823 0.024550
-0.400117 0.6898
1.374092 0.086464
15.89212 0.0000
-0.376696 0.086921 -4.333757 0.0000
Variable
C
INTEREST(-1)
INTEREST(-2)
R-squared
Adjusted R-squared
S.E. of regression
I
I
101
I
I
Equation: UNTITLED Workfile: TEMP::Untitled\
View Proc Object Print Name Freeze Estimate Forecast Stats Resids
Correlogram of Residuals
Date: 09/10/20 Time: 21:08
Sample (adjusted): 2010M03 2019M12
Q-statistic probabilities adjusted for 2 dynamic regressors
Autocorrelation Partial Correlation
C.
ים
,
I
I
I
I
0.992863 Mean dependent var
S.D. dependent var
0.992738
0.101586 Akaike info criterion
I
I
Std. Error
I
I
3 0.033 0.044 2.3044 0.512
4 0.096 0.077 3.4426 0.487
5 0.035 0.037 3.5935 0.609
6 0.020 0.038 3.6463 0.724
7 0.046 0.047 3.9158 0.789
8 0.088 0.084 4.9134 0.767
9 -0.046 -0.051 5.1861 0.818
10 -0.060 -0.046 5.6527 0.844
An AR(2) model is fitted to time series INTEREST (interest rate). The least-squares estimation results and the residual correlogram are given above.
Which statement is correct about the results?
t-Statistic Prob.
101
IDI
I
I
I
2.776102
1.192120
-1.710727
AC PAC Q-Stat Prob
1 0.039 0.039 0.1836 0.668
2 -0.128 -0.129 2.1702 0.338
Select one:
O a. The AR (2) model is not suitable, due to the evidence of autocorrelated error term
O b. The AR (2) model is suitable, due to little evidence of autocorrelated error term
The AR(2) model is not suitable, due to the evidence of heteroskedasticity
Od. The AR(1) model is suitable, due to the evidence of no heteroskedasticity
Transcribed Image Text:Dependent Variable: INTEREST Method: Least Squares Date: 09/10/20 Time: 21:08 Sample (adjusted): 2010M03 2019M12 Included observations: 118 after adjustments Coefficient -0.009823 0.024550 -0.400117 0.6898 1.374092 0.086464 15.89212 0.0000 -0.376696 0.086921 -4.333757 0.0000 Variable C INTEREST(-1) INTEREST(-2) R-squared Adjusted R-squared S.E. of regression I I 101 I I Equation: UNTITLED Workfile: TEMP::Untitled\ View Proc Object Print Name Freeze Estimate Forecast Stats Resids Correlogram of Residuals Date: 09/10/20 Time: 21:08 Sample (adjusted): 2010M03 2019M12 Q-statistic probabilities adjusted for 2 dynamic regressors Autocorrelation Partial Correlation C. ים , I I I I 0.992863 Mean dependent var S.D. dependent var 0.992738 0.101586 Akaike info criterion I I Std. Error I I 3 0.033 0.044 2.3044 0.512 4 0.096 0.077 3.4426 0.487 5 0.035 0.037 3.5935 0.609 6 0.020 0.038 3.6463 0.724 7 0.046 0.047 3.9158 0.789 8 0.088 0.084 4.9134 0.767 9 -0.046 -0.051 5.1861 0.818 10 -0.060 -0.046 5.6527 0.844 An AR(2) model is fitted to time series INTEREST (interest rate). The least-squares estimation results and the residual correlogram are given above. Which statement is correct about the results? t-Statistic Prob. 101 IDI I I I 2.776102 1.192120 -1.710727 AC PAC Q-Stat Prob 1 0.039 0.039 0.1836 0.668 2 -0.128 -0.129 2.1702 0.338 Select one: O a. The AR (2) model is not suitable, due to the evidence of autocorrelated error term O b. The AR (2) model is suitable, due to little evidence of autocorrelated error term The AR(2) model is not suitable, due to the evidence of heteroskedasticity Od. The AR(1) model is suitable, due to the evidence of no heteroskedasticity
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