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From |
"Mentzakis, Emmanouil" <e.mentzakis@abdn.ac.uk> |

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

Subject |
st: RE: RE: gllamm or else? |

Date |
Mon, 30 Jun 2008 09:43:02 +0100 |

Stefan Boes has written -regoprob- to estimate generalized ordered probit models with random effects. Regards Manos ________________________________________ From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Verkuilen, Jay [JVerkuilen@gc.cuny.edu] Sent: 28 June 2008 21:44 To: statalist@hsphsun2.harvard.edu Subject: st: RE: gllamm or else? Andrea Bennett wrote: >>What I am not sure about is how I should perform the estimation. From a theoretical standpoint, I would assume fixed effects in the first data set while for the second data set it is an open question, still (I could group for political main topics, for example). Now, in OLS I simply would include year dummies for fixed effects. But as far as I know, I should not use year dummies in gllamm (and usually not in probit/oprobit) for estimating fixed effects.<< Year dummies aren't a disaster if you don't have many of them, but of course they may not do what you need. You probably want to do cluster corrected robust standard errors (cluster by year). >>So, a) how would I include a simple fixed effect estimation in gllamm (as I understand it, using i(year) applies random effects) << -xi- or, better, -findit xi3-. >>Additionally, which gllamm options are best suited for these kind of estimation? Should I use -adapt- and should I set a specific value for -nip- ? Using only the -adapt- option (which I read results in a better estimation) already results in 15min estimation time for a sub-sample of about 3000 observations while the full data set contains 25'000 observations. I really fear this will take forever (besides that my computer dies from overheating!).<< Numerical integration is inherently slow. In general adaptive quadrature requires a lot of resources, much more so that non-adaptive quadrature. -gllamm- is also slow because of its use of numerical derivatives rather than analytic derivatives. This means estimation can be glacial. One way to speed things up is to give it good starting values. These can often be had by making a simpler model (often one that can be estimated using a different procedure, e.g., ologit) and getting coefficient values from it. For instance, using non-adaptive quadrature while you're doing model specification, erring on the side of keeping variables due to its lower accuracy, might be a viable strategy. Then fit by setting up your program on Friday before you go home with the plan to check it on Monday morning. Someone wrote a random effects version of -gologit2-. I don't know who it was or the name of the program, unfortunately. Hopefully Rich Williams will chime in.... Alternatively, other programs (such as Mplus) might be faster. * * 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/ The University of Aberdeen is a charity registered in Scotland, No SC013683. * * 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/

**References**:**st: gllamm or else?***From:*Andrea Bennett <mac.stata@gmail.com>

**st: RE: gllamm or else?***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

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