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st: Re: mixed effects model and autocorrelation


From   Kit Baum <baum@bc.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: Re: mixed effects model and autocorrelation
Date   Fri, 12 Oct 2007 06:43:28 -0400

Just as -newey- is an alternative to -prais- for a single time series, you might consider -xtivreg2- as a substitute for -xtgls-. You can either model the autocorrelation as being of unknown form with no more than L significant autocorrelations, using the bw() and kernel() options, which will deliver HAC standard errors, or you can use the cluster(country) option, which will allow for cross-panel heteroskedasticity and arbitrary correlation within each panel's error structure. See Baum, Schaffer, Stillman, BC WP 667 on my CV below. NB: you need not have any instrumental variables to use - xtivreg2- (or all regressors can instrument themselves).

This option does not handle multilevel mixed effects, but those models are essentially random-effects models, and RE models have stronger assumptions about the independence of the error components from the regressors than do the fixed-effects models supported by - xtivreg2-, available from SSC.


Kit Baum, Boston College Economics and DIW Berlin
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html


On Oct 12, 2007, at 2:33 AM, statalist-digest wrote:



Now to my question: How should I think about, and understand, the
autocorrelation in the mixed effects model? With my (somewhat limited)
experience with time-series data, I need to somehow take the
autocorrelation into account. How is this done in a mixed-effect model?
Since years are nested within countries, will the estimated standard
errors be valid?
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