Repeat Exercises 4 and 5 using the data in Table 13.5 Exercises 5 Consider the VAR models with one and two lags in Exercise 4. (a) Estimate the characteristic roots and vectors for the relevant matrices discussed in Section 14.8. Apply tests for unit roots and tests for cointegration. (b) If there is a cointegrating regression, estimate it from the characteristic vectors and also from the static regression as suggested by Granger and Engle. (c) Are the results different for the VAR models with one and two lags? Are they different from those from the static regressions? What do you conclude from these results? (d) Repeat parts (a) to (c) with the seasonally adjusted data (residuals from the regression on seasonal dummies).
Repeat Exercises 4 and 5 using the data in Table 13.5
Exercises 5
Consider the VAR models with one and two lags in Exercise 4.
(a) Estimate the characteristic roots and
(b) If there is a cointegrating regression, estimate it from the characteristic vectors and also from the static regression as suggested by Granger and Engle.
(c) Are the results different for the VAR models with one and two lags? Are they different from those from the static regressions? What do you conclude from these results?
(d) Repeat parts (a) to (c) with the seasonally adjusted data (residuals from the regression on seasonal dummies).
Exercise 4
Using the data in Table 13.4, estimate a VAR model for C, and Yt with one lag and two lags.
(a) Is the model with two lags better than the model with one lag? Use the AIC and BIC criteria (see Section 13.5). Also check for residual autocorrelations.
(b) Since the data are quarterly, regress the data on seasonal dummies, and compute the residuals. Repeat the analysis with these residuals (assuming that they are the observations).
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