Henrique,
 
I would like to express the idea exactly as you wrote, but not in diffs.
Generate the diffs you want, d_y(t-1) + d_y(t-2) + ... + d_y(t-p), the first lag and the constant, then use OLS againt d_y(t) and compute e(t) to do your test.
The result will be the same as the pointed by Allin.
Sorry for the mismatch.
 
By the way, I did it in my Master thesis, for all the models considered by D-F and KPSS (no ct, ct, ct and trend and ct and trend^2).
What exactly do you want with this procedure? I may have the tables you need...
 
Ricardo.

From: Henrique Andrade
Sent: Thursday, April 08, 2010 2:16 AM
To: Gretl list
Subject: Re: [Gretl-users] Saving ADF Residuals

Em 7 de julho 2010 Allin <cottrell@wfu.edu> escreveu:

On Wed, 7 Apr 2010, Henrique Andrade wrote:

> Is it possible to save the residuals of the Augmented
> Dickey-Fuller (ADF) test regressions? I would like to save them
> to check if they follow a white noise process.

As matters stand, you have to formulate and run the ADF regression
yourself to get access to the residuals. This goes for all
auxiliary regressions run by gretl on behalf of diagnostic test
commands: they do not create "first-class" models from which you
could access $uhat, $ess and all the rest.

This seems OK to me: if you want to investigate "second-order"
aspects of the test that's fine, but you need to run the auxiliary
regression yourself. The built-in diagnostic commands are just
convenient short-cuts for procedures that are quite easily
scripted in gretl.

I've got your point and I think you are right. Thanks Allin!


Em 8 de abril de 2010 Ricardo <ricardogs@terra.com.br> escreveu:

If the test shows that you have a unit root, by definition, the residuals doesn't follow a random walk (white nose) and vice-versa.But if you really want to see the residuals, just run an AR(p) regression, by OLS, where p is the "augmented lag" (w/o truncation), then save the residuals.

Dear Ricardo, at first I would like to thank your for your answer!

I'm interested in the residuals of the ADF test regression:

d_y(t) = b0 + (a-1)*y(t-1) + d_y(t-1) + d_y(t-2) + ... + d_y(t-p) + e(t)

This is because in the ADF test we need to choose p-lags in order to obtain residuals, e(t), that follow a white-noise process.

Um abraço,
Henrique


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