Date: 05/21/23 Time: 09:58 Sample: 7/01/1963 4/29/2022 Included observations: 14810 Autocorrelation Partial Correlation AC PAC Q-Stat Prob 1 0.076 0.076 85.556 0.000 2 0.010 0.004 86.976 0.000 3 0.009 0.008 88.308 0.000 4 -0.041 -0.042 112.80 0.000 5 0.004 0.010 113.05 0.000 6 0.008 0.007 113.95 0.000 7 0.021 0.021 120.51 0.000 8 -0.017 -0.022 124.72 0.000 9 0.000 0.003 124.72 0.000 10 -0.010 -0.010 126.15 0.000
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e., the residuals are independent). - Alternative Hypothesis (H1): There is autocorrelation in the residuals at lag k (i.e., the residuals are not independent). Show more…
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c) The following diagram shows the correlogram of the residuals for the model in (a). Autocorrelation Partial Correlation AC PAC Q-Stat Prob 1 0.594 0.594 13.445 0.000 2 0.315 -0.058 17.345 0.000 3 0.069 -0.147 17.536 0.001 4 0.075 0.172 17.768 0.001 5 -0.049 -0.197 17.870 0.003 6 -0.030 0.076 17.909 0.006 7 -0.079 -0.052 18.198 0.011 8 -0.176 -0.242 19.677 0.012 9 -0.228 0.041 22.257 0.008 10 -0.157 0.024 23.542 0.009 11 -0.072 -0.012 23.821 0.014 12 -0.227 -0.329 26.710 0.009 13 -0.176 0.180 28.527 0.008 14 -0.159 -0.083 30.078 0.007 15 -0.062 -0.052 30.326 0.011 16 -0.051 0.110 30.505 0.016 i. (3 marks) Suggest and apply a solution (or remedy) to the issue suggested by the correlogram above. Include any output here. ii. (2 marks) Did the solution work? Present an econometric evidence to support your claim.
Sri K.
The following two models have been suggested for representing some quarterly data with underlying seasonality. Model 1 Y_t = α Y_{t-4} + e_t Model 2 Y_t = β e_{t-4} + e_t where e_t is a white noise process in each case. (i) Determine the autocorrelation function for each model. [4] The observed quarterly data is used to calculate the sample autocorrelation. (ii) State the features of the sample autocorrelation that would lead you to prefer Model 1. [1] [Total 5]
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