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 = (log(price) and gwage = (log(wage).] Using the monthly data in WAGEPRC.RAW, we estimate the following distributed lag model:

(.00057) (.052) (.039) (.039)
+ .038 gwage - 3 + .081 gwage- 4 + .107 gwage -5 + .095 gwage-6
(.039) (.039) (.039) (.039)
+ .104 gwage-7 + .103 gwage -8 + .159 gwage-9 + .110 gwage-10
(.039) (.039) (.039) (.039)
+ .103 gwage-11 + .016 gwage-12
(.039) (.052)
n = 273, R2 = .317, RÌ…2 = .283.
(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 r statistics 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.)

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