Re: [Gretl-devel] coint2 with exogenous variables
Am 11.11.2011 17:44, schrieb Andreas Noack Jensen:
I have some new comments on this thread. First of all I would like
hear about the status on the question on restricting the
deterministic terms in cointegration testing. To state my view a bit
differently: If there exist any defense for testing in the
unrestricted models I would really like to know about it because I am
only aware of recommendations to avoid those models.
First of all let me state that I am fully aware of the similarity issues
in that context, which you already briefly alluded to. I also heard the
recommendations you are following, and for some time I even tried to
follow them. And there are cases where I fully agree, for example when I
have to model the German unification break.
To take the example of the unrestricted constant term, however, in the
end I think there is often not a real problem. That is, if you have data
which beyond any doubt have linear trend terms, and you are not ready to
accept any long-run equilibrium which is trend stationary instead of
mean stationary. (Because after all, how do you really interpret
economically an equilibrium which is diverging?)
Well then you say: no problem, just kick the trend term out of the CI
space after determining the rank. However with typical small-sample data
often this just doesn't work; in my real-world experience first you
often get quite strange rank test results when you allow for linear
trends in the CI space, and secondly afterwards often the restriction
test wouldn't allow you to set the trend coefficient to zero. The
alternative is to not allow the trend terms right from the start,
because you really have no use for them.
That's what we often do in econometrics: we make some specification
assumptions to gain efficiency, power and so on. And sure there is the
danger that those assumptions could be wrong, and so you really have to
think hard about them. But I don't see why a fixed rule like "thou shalt
not test for cointegration with unrestricted deterministic terms" would
be the best solution of the dilemma.
BTW, it could be argued that since I am holding small sample sizes
responsible for the dilemma, we should always use the newly available
small-sample tools like bootstrapping and Bartlett corrections. I have
some experience with that and indeed, maybe that would resolve some of
the otherwise strange test results. This may be a case to push for these
tools to be more widely available.
Thanks for initiating an interesting discussion,
Sven