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Bayesian MCMC Estimation

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Handbook of Regional Science

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.

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Author information

Authors and Affiliations

  1. Department of Economics, University of Cincinnati, Cincinnati, OH, 45221, USA

    Jeffrey A. Mills & Olivier Parent

Authors
  1. Jeffrey A. Mills
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  2. Olivier Parent
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Corresponding author

Correspondence to Jeffrey A. Mills .

Editor information

Editors and Affiliations

  1. Institute for Economic Geography and GIScience, Vienna University of Economics and Business, Vienna, Austria

    Manfred M. Fischer

  2. and Business Administration Department of Spatial Economics, Free University Faculty of Economics, Amsterdam, The Netherlands

    Peter Nijkamp

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© 2014 Springer-Verlag Berlin Heidelberg

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Mills, J.A., Parent, O. (2014). Bayesian MCMC Estimation. In: Fischer, M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23430-9_89

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  • DOI: https://doi.org/10.1007/978-3-642-23430-9_89

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23429-3

  • Online ISBN: 978-3-642-23430-9

  • eBook Packages: Business and Economics

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