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Bayesian Spatial Bivariate Panel Probit Estimation

Badi H. Baltagi ^a

Peter H. Egger ^b

Michaela Kesina ^c

^aDepartment of Economics and Center for Policy Research, Syracuse University, Syracuse, NY, USA

^bETH Zurich, Zurich, Switzerland, CEPR, CESifo

^cETH Zurich, Zurich, Switzerland

Spatial Econometrics: Qualitative and Limited Dependent Variables

ISBN: 978-1-78560-986-2, eISBN: 978-1-78560-985-5

Publication date: 1 December 2016

Abstract

This paper formulates and analyzes Bayesian model variants for the analysis of systems of spatial panel data with binary-dependent variables. The paper focuses on cases where latent variables of cross-sectional units in an equation of the system contemporaneously depend on the values of the same and, eventually, other latent variables of other cross-sectional units. Moreover, the paper discusses cases where time-invariant effects are exogenous versus endogenous. Such models may have numerous applications in industrial economics, public economics, or international economics. The paper illustrates that the performance of Bayesian estimation methods for such models is supportive of their use with even relatively small panel data sets.

Keywords

Acknowledgements

Acknowledgments

The authors would like to thank an anonymous reviewer, Cheng Hsiao, and Jaya Krishnakumar for numerous helpful comments on earlier versions of the manuscript. Moreover, we are grateful to the participants of the Advances in Econometrics Conference 2015 at Baton Rouge and the 26th (EC)2 Conference on Theory and Practice of Spatial Econometrics.

Citation

Baltagi, B.H., Egger, P.H. and Kesina, M. (2016), "Bayesian Spatial Bivariate Panel Probit Estimation", Spatial Econometrics: Qualitative and Limited Dependent Variables (Advances in Econometrics, Vol. 37), Emerald Group Publishing Limited, Leeds, pp. 119-144. https://doi.org/10.1108/S0731-905320160000037011

Publisher

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Emerald Group Publishing Limited