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st: adaptive or non-adaptive quadrature ?
I'm trying to estimate a random effects logistic model. I used both
xtlogit and gllamm for comparison.
Running xtlogit and gllamm with the same number of integration points
give this results :
sigma_v is the sd of random effects.
xtlogit adaptive quadrature : log-likelihood=-8711.7129 sigma_v=0.6145
xtlogit standard quadrature : log-likelihood =-8695.7637 sigma_v=2.122
gllamm adaptive : log-likelihood=-8695.8048 sigma_v=2.1156
gllamm standard: log-likelihood=-8695.7633 sigma_v=2.122
I know that xtlogit and gllamm use differents method of adaptive
quadrature.
I'm surprise that standard quadrature perform better than adaptive,
especially in xtlogit.
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