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Re: st: re: tsset with non-integers


From   John Antonakis <john.antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: re: tsset with non-integers
Date   Thu, 07 May 2009 19:32:14 +0200

Oh....forgot to say; welcome to Stata. You won't regret it. SPSS has some very serious limitations.

Also, I just check and saw what SPSSs command does: it seems to check for serial correlation in adjacent observations. As Kit said, unless the data are specifically ordered for that purpose, checking for seriel correlation makes no sense.

As for the clustering, I used the term in a generic sense to talk of nested observations. If observations are not independent then you need to model this non-independence using the -cluster- option for the vce. Also, intercepts might vary between clusters. If so use -xttest0- after running -xtreg- to see if you need to model the random intercept.

HTH,
John.

____________________________________________________

Prof. John Antonakis
Associate Dean Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland

Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Faculty page:
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On 07.05.2009 19:02, Frank Gallo wrote:
<>

SPSS offers users the post regression estimation option to test the independence of residuals terms using the Durbin-Watson test so that users can check the independence assumption. My data are not of the time series type. As a stata beginner, I am trying to learn a stata equivalent approach. Thank you.

Best,
Frank


On Thursday, May 07, 2009, at 09:47AM, "Nick Cox" <n.j.cox@durham.ac.uk> wrote:
Why can't you -tsset- the data? If your data are time series, you should know the times (dates). Specify the time variable to -tsset-. If they aren't, then serial correlation is presumably not defined or applicable anyway. Nick n.j.cox@durham.ac.uk
Frank Gallo

Hi Eva,

The way I understood Kit's response was that both suggested approaches (estat bgodfrey and wntestq) require the user to tsset the data, which I cannot do. I am looking to test the autocorrelation of residuals terms so that I can check the regression assumption of indepence of errors. Thank you.

On Thursday, May 07, 2009, at 09:31AM, "Eva Poen" <eva.poen@gmail.com> wrote:
Frank,

please tell us what you don't understand about the replies you have
got so far. Kit gave recommendations for testing for autocorrelation
in residuals. If it is serial correlation that you are after, you have
time series (or panel) data, and should tell Stata about it, using the
-tsset- command. See -help tsset-.

If it is not serial correlation but some other kind of dependence, you
need to be more specific about the type of data you have.

Kit discouraged the use of -durbinh-, but if you insist on using it
anyway, you need to -tsset- your data first. In any case, residuals
are very unlikely to be integers. Your _time_ variable (e.g. years or
quarters or months)  could be integer. But that has nothing to do with
your residuals.

Eva


2009/5/7 Frank Gallo <fjgallo@mac.com>:
<>

Hi Nick & Kit,

Thank you for your responses. My goal is only to check the independence of residual terms following a regression run. My reading of the durbinh command lead me to believe that it would help me achieve my goal. Can you suggest an alternative option to check the independence of residual terms that are non-integers? Thank you.

Best,
Frank



On Thursday, May 07, 2009, at 06:32AM, "Kit Baum" <baum@bc.edu> wrote:
<>
Frank said

What I would like to do, which I cannot find exactly in the archives,
is to check the independence of the residual terms (e) from a
regression. I would like to run the -durbinh- command...

Nick Cox answered the technical question re -tsset-. I do not
recommend you rely on the -durbinh- command. It is a special case of
the Breusch-Godfrey test in which you only consider AR(1) vs i.i.d.
The -estat bgodfrey- postestimation command allows you to test for
higher-order autocorrelation as well (which might well be present even
if an AR(1) coefficient is insignificant). Also consider using -
wntestq-, which is an unconditional test of the residuals'
autocorrelation function. (B-G is a conditional test in that it uses
the X matrix from the regression, whereas the Lung-Box-Pierce "Q" test
may be applied to any time series). All will require that the data are
properly -tsset-.
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