Date: Thu, 1 Oct 2009 22:50:02 -0400
Reply-To: Mark Miller <mdhmiller@GMAIL.COM>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Mark Miller <mdhmiller@GMAIL.COM>
Subject: Re: OLS estimates on clusterred data
Content-Type: text/plain; charset=ISO-8859-1
The estimation problem sounds to me like a model case
of Time-Series Cross-Section Regression for which
(SAS/ETS) Proc TSCSreg has been specifically designed.
TSCSReg provides various and flexible modeling structures which may overlap
Proc Mixed, but TSCSreg was originally aimed directly at economic
models which frequently deal with time-series of cross-section data.
... Mark Miller
On Thu, Oct 1, 2009 at 3:03 PM, Paige Miller <firstname.lastname@example.org> wrote:
> On Oct 1, 2:40 pm, Tony <tony.cross...@gmail.com> wrote:
> > Thanks!
> > Can i just do the following?
> > PROC MIXED DATA=imputdata NOitprint;
> > CLASS year state;
> > MODEL Y= X;
> > RUN;
> Unless you put Year and State in the model statement, you have a
> simple linear regression of Y versus X.
> Even if you put Year and State in the model, this doesn't account for
> the claimed serial correlation over the years, nor does it account for
> any clustering, which I assume is different than the serial
> correlation you are referring to, but which you don't explain further.
> There are many different types of covariance structures possible in
> PROC MIXED, including some that deal with autocorrelation. Check the
> docs to see if one of them meets your needs.
> Paige Miller
> paige\dot\miller \at\ kodak\dot\com