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From |
"Filippo Maria D'Arcangelo" <[email protected]> |

To |
[email protected] |

Subject |
Re: Re: st: information criterions after -xtreg, re- |

Date |
Wed, 22 Jan 2014 18:43:06 +0100 (CET) |

Hi Alfonso, thank you very much. I'd prefer to use -xtreg, re- to calculate my s.e. because of some assumptions on my error distribution. If I understood it correctly, I can perform the FGLS estimation of my random effect model, while computing the likelihood by estimating the (equivalent) ML model. In case the estimates of coefficients differ from -re- and -mle- I should suspect that something is going on and probably refuse to use the AIC calculated by ML for choosing between FGLS models. I hope it makes sense. My doubts originated from the fact that Stata stores log-likelihood after many least-squares estimations (such as after -regress-), and I wondered why it was not the case with -xtreg, re-. f. >Hi Filippo, > >If you want to estimate random effects -xtreg, mle- does that using maximum likelihood. -xtreg, re- uses feasible GLS >to do the random effects estimation. In many cases the maximum likelihood is more efficient and iterated FGLS >converges to the maximum likelihood estimate. I say many cases because this doesn't always hold. So if you're >interested in a comparison criterion for different random effects models you can always estimate them with -mle- and >use AIC. You can always check how close your estimates of the coefficients are between a -re-and an -mle- estimation >by running both. > >Best, > >Alfonso Sanchez-Penalver >> On Jan 22, 2014, at 5:40 AM, "Filippo Maria D'Arcangelo" <[email protected]> wrote: >> >> Dear statlisters, >> >> I have a panel dataset and I am running a set of regressions. >> They are "nested", in the sense of Wooldridge(2005), i.e. the covariates varies incrementally from a simpler model to a more "rich" one. >> For this purpose, I am using -xtreg, re-, assuming random effects toward the panel groups. >> I want to compare the regressions, using some sort of (not too fancy) criterion. >> >> I know that a useful criterion could be Akaike's Information Criterion (AIC). I am also aware that there are a few alternatives, mostly relying on Kullback–Leibler divergence, which in turns (to the extent of my limited knowledge) relies on a likelihood calculation. >> >> However, -xtreg, re- does not store any likelihood measure (and therefore, no AIC). >> Other options of -xtreg- do have this feature (e.g. [, fe] and definitely [, mle]). >> Is there any theoretical reason for the likelihood not to be calculated after a random effect regression? >> >> Should I use something different instead, such as a goodness-to-fit criterion, i.e. adjusted-R^2? If yes, does anybody of you knows how to correct determine this parameter (adj-R^2) for panel data? >> Thank you, >> >> >> Filippo Maria D'Arcangelo >> >> >> Reference: Wooldridge, Jeffrey M. (2005) "Introductory Econometrics. A Modern Approach". p. 193 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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