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From | Stas Kolenikov <skolenik@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: gllamm, xtmixed, and level-2 standard errors |
Date | Tue, 16 Nov 2010 18:28:54 -0600 |
What if you run -xtreg, re- or -xtreg, mle-? They will give you the same model, too. On Tue, Nov 16, 2010 at 4:27 PM, Trey Causey <tcausey@uw.edu> wrote: > Greetings all. I am estimating a two-level, random-effects linear > model. I know that gllamm is not the most computationally efficient > option for this, but I am running into some very weird problems. I > have ~21,000 individuals nested in 16 countries. I have 9 > individual-level predictors (listed as ind1-9) and 2 country-level > predictors (listed as c1 and c2). When I estimate the model using > gllamm, here are my results: > > . gllamm DV ind1 ind2 ind3 ind4 ind5 ind6 ind7 ind8 ind9 c1 c2,i(id) > adapt nip(16) > > Running adaptive quadrature > Iteration 0: log likelihood = -22865.024 > Iteration 1: log likelihood = -22841.735 > Iteration 2: log likelihood = -22807.82 > Iteration 3: log likelihood = -22797.118 > Iteration 4: log likelihood = -22794.274 > Iteration 5: log likelihood = -22792.672 > Iteration 6: log likelihood = -22791.582 > Iteration 7: log likelihood = -22791.557 > Iteration 8: log likelihood = -22791.428 > Iteration 9: log likelihood = -22791.426 > > > Adaptive quadrature has converged, running Newton-Raphson > Iteration 0: log likelihood = -22791.426 (not concave) > Iteration 1: log likelihood = -22791.426 (not concave) > Iteration 2: log likelihood = -22789.86 > Iteration 3: log likelihood = -22789.371 > Iteration 4: log likelihood = -22788.767 > Iteration 5: log likelihood = -22788.613 > Iteration 6: log likelihood = -22788.604 > Iteration 7: log likelihood = -22788.604 > > number of level 1 units = 21360 > number of level 2 units = 16 > > Condition Number = 433.81863 > > gllamm model > > log likelihood = -22788.604 > > ------------------------------------------------------------------------------ > DV | Coef. Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > ind1 | -.0020515 .000392 -5.23 0.000 -.0028198 -.0012833 > ind2 | -.3839988 .010841 -35.42 0.000 -.4052468 -.3627508 > ind3 | -.079134 .0113476 -6.97 0.000 -.1013749 -.0568931 > ind4 | .0800358 .0109386 7.32 0.000 .0585966 .101475 > ind5 | .0468417 .0048978 9.56 0.000 .0372423 > .0564411 > ind6 | .1685022 .0149735 11.25 0.000 .1391546 .1978497 > ind7 | -.2057474 .0171485 -12.00 0.000 -.2393579 -.1721368 > ind8 | -.093775 .0094251 -9.95 0.000 -.1122479 -.0753021 > ind9 | -.0080367 .0021554 -3.73 0.000 -.0122613 -.0038122 > c1 | .762577 .0802034 9.51 0.000 .6053813 .9197727 > c2 | .1763846 .0664327 2.66 0.008 .0461789 .3065903 > _cons | 1.265279 .1023452 12.36 0.000 1.064686 1.465872 > ------------------------------------------------------------------------------ > > Variance at level 1 > ------------------------------------------------------------------------------ > > .49269203 (.00476915) > > Variances and covariances of random effects > ------------------------------------------------------------------------------ > > > ***level 2 (id) > > var(1): .09866295 (.01101541) > ------------------------------------------------------------------------------ > > When I estimate the model using xtmixed or xtreg, the output is > essentially the same until I get to the country-level predictors; the > coefficients are slightly different and the standard errors are > approximately *ten* times smaller: > > . xtmixed DV ind1 ind2 ind3 ind4 ind5 ind6 ind7 ind8 ind9 c1 c2 || id:,mle > Performing EM optimization: > Performing gradient-based optimization: > Iteration 0: log likelihood = -22785.965 > Iteration 1: log likelihood = -22785.965 > Computing standard errors: > Mixed-effects ML regression Number of obs = 21360 > Group variable: id Number of groups = 16 > Obs per group: min = 730 > avg = 1335.0 > max = 2875 > > Wald chi2(11) = 2296.06 > Log likelihood = -22785.965 Prob > chi2 = 0.0000 > ------------------------------------------------------------------------------ > DV | Coef. Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > ind1 | -.0020472 .0003917 -5.23 0.000 -.002815 -.0012794 > ind2 | -.3840113 .0108422 -35.42 0.000 -.4052615 -.362761 > ind3 | -.0790874 .0113578 -6.96 0.000 -.1013483 -.0568264 > ind4 | .0799408 .0109411 7.31 0.000 .0584966 .101385 > ind5 | .0468955 .0048961 9.58 0.000 .0372994 .0564916 > ind6 | .1686695 .0149734 11.26 0.000 .1393222 .1980167 > ind7 | -.2054921 .0172501 -11.91 0.000 -.2393018 -.1716824 > ind8 | -.0941011 .0093698 -10.04 0.000 -.1124655 -.0757367 > ind9 | -.0079976 .0021584 -3.71 0.000 -.0122279 -.0037672 > c1 | .6718781 .2659761 2.53 0.012 .1505744 1.193182 > c2 | .1812668 .1083347 1.67 0.094 -.0310652 .3935988 > _cons | 1.306302 .2079643 6.28 0.000 .8986998 1.713905 > ------------------------------------------------------------------------------ > ------------------------------------------------------------------------------ > Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] > -----------------------------+------------------------------------------------ > id: Identity | > sd(_cons) | .2033876 .0363049 .1433454 .2885792 > -----------------------------+------------------------------------------------ > sd(Residual) | .7019342 .0033974 .695307 .7086246 > ------------------------------------------------------------------------------ > LR test vs. linear regression: chibar2(01) = 1684.50 Prob >= chibar2 = 0.0000 > > > This is obviously a big problem for establishing significance. I have > read previous threads about this problem with xtlogit but have not > seen it mentioned for linear models nor I have a seen a solution. It > is not immediately clear to me why the estimates or standard errors > should differ at all -- as Rabe-Hesketh and Skrondal say in their > book, gllamm is not as computationally efficient for linear models but > the results should be essentially the same. I have replicated this in > Stata 10 and Stata 11. > > Thank you very much. > Trey > ----- > Trey Causey > Department of Sociology > University of Washington > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/