## Info

a. From the preceding results, what can you say about the nature of autocorrelation in the wages-productivity data?

b. If you think that an AR(1) mechanism characterizes autocorrelation in our data, would you use the first-difference transformation to get rid of autocorrelation? Justify your answer.

Problems

12.26. Refer to the data on the copper industry given in Table 12.7.

a. From these data estimate the following regression model:

ln Ct = 01 + 02 ln I + 03 ln Lt + 04 ln H + 05 ln At + ut

### Interpret the results.

b. Obtain the residuals and standardized residuals from the preceding regression and plot them. What can you surmise about the presence of autocorrelation in these residuals?

c. Estimate the Durbin-Watson d statistic and comment on the nature of autocorrelation present in the data.

d. Carry out the runs test and see if your answer differs from that just given in c.

e. How would you find out if an AR(p) process better describes autocorrelation than an AR(1) process?

Note: Save the data for further analysis. (See exercise 12.28.)

12.27. You are given the data in Table 12.8.

a. Verify that Durbin-Watson d = 0.4148.

b. Is there positive serial correlation in the disturbances?

CHAPTER TWELVE: AUTOCORRELATION 499

TABLE 12.7 DETERMINANTS OF U.S. DOMESTIC PRICE OF COPPER, 1951-1980

Year

0 0

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