On Thu, 20 Feb 2014, Sven Schreiber wrote:
 Am 20.02.2014 12:09, schrieb smyks:
> I have checked with denmark.gdt dataset:
> coint 10 1 2 --verbose --test-down  <= Gretl reports sample size of 44
> coint 10 1 2 --verbose --test-down --skip-df <= Gretl reports sample
> size of 53
 Ok I now see what you mean, effectively different samples are used in
 what should be the same approach, like you said. I don't see a
 justification for that (i.e. I tend to agree it's a bug).
> To my understanding both command lines should employ the same sample
> size of 53, which is in line with Hayashi (2000).
 Actually, when looking at the different samples that are used in the
 various steps of the procedure I think they're debatable in general. If
 you pick a max lag of 10 I would actually tend to favor the shortest
 sample (T=44) instead of the longest (T=53), because only the shortest
 is the common denominator for everything. Of course, if results differ
 wildly, it's a sign of problems anyway. 
I've now fixed the breakage in the --test-down option for "coint", so that 
it works in the same way as for "adf". (When I updated adf a while back, 
I'm afraid I just forgot about coint.)
On this sample-size issue, I see what's happening. When the --skip-df 
option is not given we run initial (A)DF tests on two or more variables, 
and we make an effort to impose a uniform sample range, which is then also 
applied to the final test of the residuals from the cointegrating 
regression. Under --skip-df, when only one (A)DF test is run, we just let 
the sample default to the longest available.
I agree that the resulting difference in the Engle-Granger test is hard to 
justify, but like Sven I'm not sure what the "right" fix is. Obviously 
testing for the minimized information criterion must be done on a uniform 
sample range that reserves the maximum number of lags. If we then -- 
having found the optimal lag, k* -- switch to the longest sample 
consistent with that lag, we're arguably being inconsistent since we might 
have got a different k* if we tested on a longer sample in the first 
place.
Any other thoughts on this?
Allin Cottrell