semiparametric Bayesian inference in autoregressive panel data models

摘要

This paper develops Bayesian methods for inference in dynamic panel data models with individual effects, and applies them to study longitudinal data on earnings from the Panel Study of Income Dynamics (PSID). We study semiparametric versions of commonly used random effects autoregressive models, in which the distribution o the disturbances is not restricted to fall in a parametric class. To model the unknown distributions without resorting to strong parametric assumptions, we draw upon recent advances in the theory and computation of nonparametric Bayesian models using Dirichlet process priors. The overall approach can be viewed as an application of the general semiparametric Bayesian approach of West, Muller, and Escobar (1994) which in turn makes use of results on Bayesian density estimation by Escobar (1994) and Escobar and West (1995).