Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects
具有嵌套随机效应的受限与离散因变量模型的最大似然估计
Gauss-Hermite quadrature is often used to evaluate and maximize the likelihood for random component probit models. Unfortunately, the estimates are biased for large cluster sizes and/or intraclass correlations. We show that adaptive quadrature largely overcomes these problems. We then extend the adaptive quadrature approach to general random coefficient models with limited and discrete dependent variables. The models can include several nested random effects (intercepts and…