Semiparametric Hierarchical Bayes Analysis of Discrete Panel Data with State Dependence and Serial Correlation

In this paper we consider the analysis of semiparametric models for binary panel data with state dependence and serial correlation. A hierarchical approach is used in addressing heterogeneity, dealing with the initial conditions, and incorporating correlation between the covariates and the random effects. We consider a semiparametric specification in which a Markov process smoothness prior is used to model an unknown regression function. The paper presents new computationally efficient Markov chain Monte Carlo estimation algorithms. Simulation results suggest that the methods perform well. In addition to estimation, we address the problem of model choice and compare competing parametric and semiparametric specifications. Moreover, we present a framework for calculating the average covariate effects, which deals with the nonlinearity and dynamic structure of the model. The techniques of this paper are used to study the intertemporal labor force participation decisions of a panel of 1545 married women. In this application, the data support a semiparametric model with multiple sources of heterogeneity and multi-lag state dependence.