Using gret l for Principles of Econometrics, 4th Edition
Serial Correlation in Residuals
The correlogram can also be used to check whether the assumption that model errors have zero covariance-an important assumption in the proof of the Gauss-Markov theorem. The example that illustrates this is based on the Phillips curve that relates inflation and unemployment. The data used are from Australia and reside in the phillips-aus. gdt dataset.
The model to be estimated is
inf = ві + в2 Аи* + et (9.6)
The data are quarterly and begin in 1987:1. A time-series plot of both series is shown below in Figure 9.10. The graphs show some evidence of serial correlation in both series.
|
|
The model is estimated by least squares and the residuals are plotted against time. These appear in Figure 9.11. A correlogram of the residuals that appears below seems to confirm this. To generate the regression and graphs is simple. The script to do so is:
ols inf const d_u
2 series ehat = $uhat
3 gnuplot ehat —time-series
4 corrgm ehat
Unfortuantely, gretl will not accept the accessor, $uhat, as an input into either gnuplot or corrgm. That means you have to create a series, ehat, first. Once this is created, both functions work as expected.
