(i) Using the data in WAGEPRC.RAW, estimate the distributed lag model from Problem. Use re...

(i) Using the data in WAGEPRC.RAW, estimate the distributed lag model from Problem. Use regression (12.14) to test for AR(1) serial correlation.

(ii) Reestimate the model using iterated Cochrane-Orcutt estimation. What is your new estimate of the long-run propensity?

(iii) Using iterated CO, find the standard error for the LRP. (This requires you to estimate a modified equation.) Determine whether the estimated LRP is statistically different from one at the 5% level.

Problem For the U.S. economy, let gprice denote the monthly growth in the overall price level and let gwage be the monthly growth in hourly wages. [These are both obtained as differences of logarithms: gprice = Δlog(price) and gwage = Δlog(wage).] Using the monthly data in WAGEPRC.RAW, we estimate the following distributed lag model:

(i) Sketch the estimated lag distribution. At what lag is the effect of gwage on gprice largest? Which lag has the smallest coefficient?

(ii) For which lags are the tstatistics less than two?

(iii) What is the estimated long-run propensity? Is it much different than one? Explain what the LRP tells us in this example.

(iv) What regression would you run to obtain the standard error of the LRP directly?

(v) How would you test the joint significance of six more lags of gwage? What would be the dfs in the F distribution? (Be careful here; you lose six more observations.)