Introductory Econometrics 4th Edition by Jeffrey Wooldridge

Exercise 3

In Computer Exercise, you estimated a simple relationship between consumption growth and growth in disposable income. Test the equation for AR(1) serial correlation (using CONSUMP.RAW).
(ii) In Computer Exercise, you tested the permanent income hypothesis by regressing the growth in consumption on one lag. After running this regression, test for heteroskedasticity by regressing the squared residuals on gc t-1 and gc t-1 2. What do you conclude
Exercise Use the data set CONSUMP.RAW for this exercise.
(i) Estimate a simple regression model relating the growth in real per capita consumption (of nondurables and services) to the growth in real per capita disposable income. Use the change in the logarithms in both cases. Report the results in the usual form. Interpret the equation and discuss statistical significance.
(ii) Add a lag of the growth in real per capita disposable income to the equation from part (i). What do you conclude about adjustment lags in consumption growth
(iii) Add the real interest rate to the equation in part (i). Does it affect consumption growth
Exercise Use CONSUMP.RAW for this exercise. One version of the permanent income hypothesis (PIH) of consumption is that the growth in consumption is unpredictable. [Another version is that the change in consumption itself is unpredictable; see Mankiw (1994, Chapter 15) for discussion of the PIH.] Let gc t = log(c t ) -log(c t-1 ) be the growth in real per capita consumption (of nondurables and services). Then the PIH implies that E = E(gc t ), where I t-1 denotes information known at time (t-1); in this case, t denotes a year.
(i) Test the PIH by estimating gc t = 0 + 1 gc t-1 + u t. Clearly state the null and alternative hypotheses. What do you conclude
(ii) To the regression in part (i), add gy t-1 and i3 t-1 , where gyt is the growth in real per capita disposable income and i3t is the interest rate on three-month T-bills;
note that each must be lagged in the regression. Are these two additional variables jointly significant