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Bayesian MCMC Estimation

  • Jeffrey A. Mills
  • Olivier Parent
Reference work entry

Abstract

This chapter provides a survey of the recent literature on Bayesian inference methods in regional science. This discussion is presented in the context of the Spatial Durbin Model (SDM) with heteroskedasticity as a canonical example. The overall performance of different hierarchical models is analyzed. We extend the benchmark specification to the dynamic panel data model with spatial dependence. An empirical illustration of the flexibility of the Bayesian approach is provided through the analysis of the role of knowledge production and spatiotemporal spillover effects using a space-time panel data set covering 49 US states over the period 1994–2005.

Keywords

Markov Chain Markov Chain Monte Carlo Spatial Dependence Markov Chain Monte Carlo Method Transition Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of CincinnatiCincinnatiUSA

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