Sequential numerical integration in nonlinear state space models for microeconometric panel data
讨论一类含自回归误差的非线性面板数据模型的估计，提出基于序贯高斯求积的似然函数近似算法，在有序Logit和二元Probit模型的蒙特卡洛研究中表现良好，对健康经济学等应用有用。
Abstract This paper discusses the estimation of a class of nonlinear state space models including nonlinear panel data models with autoregressive error components. A health economics example illustrates the usefulness of such models. For the approximation of the likelihood function, nonlinear filtering algorithms developed in the time‐series literature are considered. Because of the relatively simple structure of these models, a straightforward algorithm based on sequential Gaussian quadrature is suggested. It performs very well both in the empirical application and a Monte Carlo study for ordered logit and binary probit models with an AR(1) error component. Copyright © 2008 John Wiley & Sons, Ltd.