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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
Comments: To: Paige Miller <paige.miller@kodak.com>
In-Reply-To:  <98d5bf58-b6d8-44fa-b98a-af9a6797e888@h13g2000yqk.googlegroups.com>
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.

http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/etsug_intro_sect026.htm

TSCSReg provides various and flexible modeling structures which may overlap with 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 <paige.miller@kodak.com> 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 >


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