[Gretl-users] panel unit root tests in CVS

The topic of testing for unit roots in panel data came up here not so long ago. In CVS and the gretl snapshots for Windows and OS X there's now code in place for doing such testing. It's not in the GUI yet (and neither is it documented), but is implemented as an extension to the existing "adf" command. If anyone's interested in testing, I'd appreciate any comments. Here's the story. If you issue the "adf" command with panel data you get (a) a brief account of the test for each panel unit plus (b) a joint test for al units, if possible. (The per-unit test output should probably be suppressed if there's an excessive number of units in the panel, but right now you always get it.) A couple of restrictions. First, with the regular adf test for time-series data you can supply a list containing more than one series and get multiple tests, but with panel data we do only one series per command; if the input list contains more than one series we ignore all but the first one. Second, the regular adf command defaults to performing 3 variants of the test -- with constant, with linear trend, and with quadratic trend -- though you can change that via the command options. With panel data we only do one variant at a time, by default the test with constant. Some details: The joint tests have the null hypothesis that the selected series is non-stationary for all panel units; the alternative is that the series is stationary for at least one unit. The test statistics are taken from Im, Pesaran and Shin (Journal of Econometrics, 2003) and Choi (Journal of International Money and Finance, 2001). The IPS test involves computing the average value of the Dickey-Fuller t-statistic (t_bar). If the lag order for the test is non-zero, t_bar is referred to the distribution that IPS call W_tbar, which is asymptotically N(0,1). If the lag order is zero but the time-series length differs across the units, we use the IPS Z_tbar statistic, which again is N(0,1) asymptotically. If the order is zero and T is the same for all units we report exact critical values as tabulated in the IPS article. (Note that the IPS test cannot be done if the --gls option to the adf command is given. There are also constraints on the minimal T for each unit.) The other test reported (Choi) is based on the p-values from the per-unit Dickey-Fuller tests. Three variants are given: the inverse chi-square, inverse normal and logit tests. These differ in how the p-values are "aggregated" and the distribution that the resulting statistic follows. For the inverse chi-square test the null is rejected if the chi-square value is big enough; for the others it is rejected if the test statistic takes on a large enough negative value (which is also the case for all variants of the IPS test). I've tested some of this against the ipshin.ado extension that's available for stata. We're pretty close in the cases I've tried but I'm not sure that ipshin gets everything right so I'm not necessarily trying to replicate exactly it reports. If anyone can test against other IPS or Choi test implementations that would be of interest. Allin Cottrell