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From |
"Steve Stillman" <stillman@motu.org.nz> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: random effects, fixed effects and mixed effects models |

Date |
Fri, 14 Oct 2005 15:49:06 +1300 |

GLLAMM and XTMIXED are capable of estimating a wide-range of models, but in general, these models are in the random effects family and assume that c is orthogonal to x. I am not sure what you mean by "I have too few observations". There is nothing in particular about fixed effects models that require more observations than OLS or random effects (assuming that you have at least two time periods of data on many/most individuals/firms). Steve -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Shuaizhang Feng Sent: Friday, October 14, 2005 8:17 AM To: statalist@hsphsun2.harvard.edu Subject: st: random effects, fixed effects and mixed effects models Dear all: I have a general question on what to choose with a panel data model like this: y(it)=x(it)b+c(i)+v(it) If I use xtreg to estimate this, when using random effects models (re), then I am assuming that c is orthogonal to x, right? But that is some assumption I am reluctant to make. I do not want to use fixed effects estimates too because I have too few observations. So could I use GLLAMM or XTMIXED. I suppose in those c is not assumed to be orthogonal to x. Is that true? also, if both GLLAMM and XTMIXED work, which one should I choose? I may have to use a two level mixed effects model like this one. y(ijt)=x(ijt)b+c1(ij)+c2(i)+v(ijt). Thanks a lot. SZ __________________________________ Yahoo! Mail - PC Magazine Editors' Choice 2005 http://mail.yahoo.com * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: random effects, fixed effects and mixed effects models***From:*Shuaizhang Feng <fengsz@yahoo.com>

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