[Gretl-users] ADF p-values difference

Dear Gretl list Finally I put my question on the differences observed in the p-value in theGretl ADF test, in Eviews and R package fUnitRoots forums (r-sig-finance). list (on the advice of Sven) if interest for the whole list "Your reading of MacKinnon's comment about finite sample ADF values is generally correct, though there is no evidence presented that they are better or worse than the asymptotic values for the t-stat. I will point out that the one case (z-stat) where MacKinnon strongly cautions against using the finite sample values is not a test statistic that EVIews produces for the ADF (though we do report related tests in the cointegration context -- perhaps in this case we shouldn't...). I think that the jury is still out on whether the t-statistic finite sample or asymptotic values are better. To provide some context, the basic idea is that that the finite sample critical values are based on a set of simulations for which MacKinnon did not employ ADF regressions. Were he to have run some with ADF corrections he might very well have found that the finite sample DF results were closer than the asymptotic results in some cases (but understandably he did not run those simulations as the number of simulations that he did run is already quite large and it is not clear the best way to set up the correlation structure for evaluating the test statistics). The most compelling argument for continuing to use the finite sample values for the ADF is, I think, one of comparability. One concern with switching over to the asymptotic values for ADF tests is that if you were to run a DF test for a smallish sample and then add a single ADF lag, you are more likely to get quite different results if we were to switch to using the asymptotic values--and in the absence of simulation results it is not clear whether this is a good or a bad thing. It would then be difficult to evaluate whether the difference in results is the result of the autocorrelation correction or the result of different critical values (or both). With either the finite sample or asymptotic choice, one is in a bit of a bind in the absence of finite sample simulations. By sticking with the finite sample values we are at least holding one thing somewhat constant...this may or may not be better... As I write this, it occurs to me that one possibility would be to report both values. That has it's own set of issues but would then allow users to pick what they want to evaluate. I'll put it on a list of things to consider." To be continued... José F. Perles University of Alicante (Spain)