Bayesian Analysis of Spatial Panel Autoregressive Models With Time-Varying Endogenous Spatial Weight Matrices, Common Factors, and Random Coefficients

Abstract

This article examines spatial panel autoregressive (SAR) models with dynamic, time-varying endogenous spatial weights matrices, common factors, and random coefficients. An empirical application is on the spillover effects of state Medicaid spending. Endogeneity of spatial weights matrices comes from the correlation of “economic distance” and the disturbances in the SAR equation. Common factors control for common shocks to all states and random coefficients may capture heterogeneity in responses. The Bayesian Markov chain Monte Carlo (MCMC) estimation is developed. Identification of factors and factor loadings, and model selection issues based upon the deviance information criterion (DIC) are explored. We find that a state’s Medicaid related spending is positively and significantly affected by those of its neighbors. Both welfare motivated move and yardstick competition are possible sources of strategic interactions among state governments. Welfare motivated move turns out to be more a driving force for the interdependence and states do exhibit heterogenous responses.

KEY WORDS:

• Bayesian estimation
• Common factors
• Deviance Information Criterion
• Time-varying endogenous spatial weight matrix
• Random coefficients
• Spatial dynamic panel model

ACKNOWLEDGMENTS

The authors thank the co-editor, the guest editor, and an anonymous referee for helpful comments.