Bayesian spatial bivariate panel probit estimation

Badi H. Baltagi, Peter H. Egger, Michaela Kesina

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)119-144
Number of pages26
JournalAdvances in Econometrics
StatePublished - 2016


  • Multivariate probit
  • Panel probit
  • Spatial econometrics

ASJC Scopus subject areas

  • Economics and Econometrics


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