Center for Policy Research
Bayesian Spatial Bivariate Panel Probit Estimation
Badi H. Baltagi, Peter H. Egger & Michaela Kesina
C.P.R. Working Paper 187
Badi H. Baltagi
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.
Feb 16, 2022