*
* Empirical example from pp 122-125
*
open data consump.dat
calendar(a) 1950
data(format=prn,org=columns) 1950:1 1993:1 year y c
*
linreg c
# constant y
prj fitted
graph(footer="Figure 5.4 Residuals")
# %resids
*
* %resids refers to the series of residuals from the most recent regression.
* Since we'll be doing quite a few auxiliary regressions and still need the
* original OLS residuals, we'll save them into a permanent series
*
set u = %resids
*
linreg u
# u{1}
*
ar1(rho=.792) c
# constant y
*
* Prais-Winsten is only available as an iterated procedure
*
ar1(method=pw) c
# constant y
ar1(method=maxl) c
# constant y
*
* Durbin's method
*
linreg c
# c{1} constant y{0 1}
*
* Breusch-Godfrey test
*
linreg u
# constant y u{1}
cdf(title="Breusch-Godfrey Serial Correlation Test") chisqr %trsquared 1
*
* OLS estimates but with HAC standard errors allowing for up to three lags
*
linreg(lwindow=newey,lags=3,robust) c
# constant y