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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: GLLAMM multinomial: tremendous instability |

Date |
Mon, 24 Nov 2008 15:02:42 +0000 (GMT) |

One thing that could cause the instability is that the estimated correlation between var1 and var2 is de facto 1. That is a clear indication of trouble. So you might want to look into what that correlation should be, and whether it should be included in your model. Another recommendation that is often made in these circumstances is to increase the number of integration points. Hope this helps, Maarten --- KONSTANTARAS KONSTANTINOS <dinokon@otenet.gr> wrote: > In my model runs (using Intercooled Stata 9.2) I experience > tremendous instability in gllamm results, higher than has been > elsewhere reported at statalist: I run a simple multinomial logit > model with random effects, using the results as initial matrix for > exactly the same model, to get as a result an extremely different log > likelihood and coefficients etc. The resulting loglikelihood differs > by large margins (3-9%). I have also tried to use exactly the same > initial values from mlogit but not only does the overall > loglikelihood continues to be unstable but also the same happens with > the loglikelihood obtained without random effects â??running gllamm > with init option-. Nevertheless the model invariably converges, > albeit to an entirely different estimate. > > I have witnessed this instability in my initial model consisting of 8 > covariates and three outcomes, hence, following the typical in > relative cases recommendation from statalist to start â??smallâ?? I > have only used 1 explanatory variable, but instability remains â??see > for example the output below-. > > This problem, the way I read it, does not lie on the data set, as I > get stable results from either mlogit -without random effects- or > asmprobit -with random effects- converging steadily and nicely > irrespective of starting matrix. By the way, asmprobit indicates > existence of unobserved heterogeneity. Hence itâ??s not a multimodal > maximum likelihood problem, or sparseness, because it would show up > more intensely in MSL methods. > > Then what might the problem be? Any suggestions on how to go about it > -because I would like to use the discrete Latent class unobserved > heterogeneity capability of gllamm-? > > Thanks in advance for your time taken to assist me! > > Dino Konstantaras > > > PS. > I use panel data consisting of 390 highly unbalanced observations > with gaps from 129 -level 1- units (id). Outliers (as detected by > hadimvo routine) have been removed. My dependent categorical variable > assumes 3 distinct values 1-3, 3 being the base reference with the > highest frequency. My covariates consist of two binary and six > continuous covariates â??standardized- . The example below has been > run with one continuous standardized covariate. The lower EPV (events > per variable) in the run below is 30. > > > * GLLAMM output * > > . gllamm alt fmt0 , expand(chosen patt m) l(mlogit) f(bin > > om) adapt i(firm) nip(8) base(3) nrf(2) eqs(a1 a2) > > Running adaptive quadrature > Iteration 0: log likelihood = -297.50547 > â?¦ > Iteration 5: log likelihood = -295.55745 > Adaptive quadrature has converged, running Newton-Raphson > Iteration 0: log likelihood = -295.55745 > Iteration 1: log likelihood = -295.55744 > Iteration 2: log likelihood = -295.55743 > number of level 1 units = 1170 > number of level 2 units = 129 > Condition Number = 18.400152 > gllamm model > log likelihood = -295.55743 > > ------------------------------------------------------------------------------ > alt | Coef. Std. Err. z P>|z| [95% Conf. > Interval] > -------------+---------------------------------------------------------------- > c1 | > fmt0 | 1.785588 2.034928 0.88 0.380 -2.202797 > 5.773973 > _cons | .3614228 .2573106 1.40 0.160 -.1428968 > .8657423 > -------------+---------------------------------------------------------------- > c2 | > fmt0 | 3.625611 1.826175 1.99 0.047 .0463731 > 7.204849 > _cons | .3541614 .2666299 1.33 0.184 -.1684235 > .8767463 > ------------------------------------------------------------------------------ > Variances and covariances of random effects > ------------------------------------------------------------------------------ > ***level 2 (firm) > var(1): .12708114 (.66898288) > cov(2,1): .16044789 (.83246548) cor(2,1): 1 > var(2): .2025755 (1.0658552) > ------------------------------------------------------------------------------ > > . mat a0=e(b) > > . gllamm alt fmt0, expand(chosen patt m) l(mlogit) f(bin > > om) adapt i(firm) nip(8) base(3) nrf(2) eqs(a1 a2) from(a0) > > Running adaptive quadrature > Iteration 0: log likelihood = -293.33182 > â?¦ > Iteration 3: log likelihood = -288.31959 > Adaptive quadrature has converged, running Newton-Raphson > Iteration 0: log likelihood = -288.31959 > Iteration 1: log likelihood = -288.31959 > Iteration 2: log likelihood = -288.31958 > > number of level 1 units = 1170 > number of level 2 units = 129 > Condition Number = 17.167532 > gllamm model > log likelihood = -288.31958 > > ------------------------------------------------------------------------------ > alt | Coef. Std. Err. z P>|z| [95% Conf. > Interval] > -------------+---------------------------------------------------------------- > c1 | > fmt0 | 5.718242 1.914655 2.99 0.003 1.965587 > 9.470898 > _cons | .585764 .2601162 2.25 0.024 .0759457 > 1.095582 > -------------+---------------------------------------------------------------- > c2 | > fmt0 | 2.66638 1.824098 1.46 0.144 -.9087856 > 6.241546 > _cons | .8559608 .2636665 3.25 0.001 .3391839 > 1.372738 > ------------------------------------------------------------------------------ > > Variances and covariances of random effects > ------------------------------------------------------------------------------ > ***level 2 (firm) > var(1): 5.508e-13 (1.743e-06) > cov(2,1): 9.007e-13 (2.777e-06) cor(2,1): .99998561 > var(2): 1.473e-12 (4.498e-06) > ------------------------------------------------------------------------------ > > > > . > > > * > * 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/ > ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room N515 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * 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/

**References**:**st: GLLAMM multinomial: tremendous instability***From:*KONSTANTARAS KONSTANTINOS <dinokon@otenet.gr>

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