An important but often overlooked obstacle in multivariate discrete data models is the specification of endogenous covariates. Endogeneity can be modeled as latent or observed, representing competing hypotheses about the outcomes being considered. However, little attention has been applied to deciphering which specification is best supported by the data. This paper highlights the use of existing Bayesian model comparison techniques to investigate the proper specification for endogenous covariates and to understand the nature of endogeneity. Consideration of both observed and latent modeling approaches is emphasized in two empirical applications. The first application examines linkages for banking contagion and the second application evaluates the impact of education on socioeconomic outcomes.
Special thanks to Ivan Jeliazkov for his invaluable guidance. I am also grateful to David Brownstone, Sean Dowsing, Ben Gillen, Dale Poirier, Arshad Rahman, Gary Richardson, Michael Sacks, and participants at the Advances in Econometrics Conference on Bayesian Model Comparison for their helpful comments.
Vossmeyer, A. (2014), "Determining the Proper Specification for Endogenous Covariates in Discrete Data Settings", Bayesian Model Comparison (Advances in Econometrics, Vol. 34), Emerald Group Publishing Limited, Bingley, pp. 223-247. https://doi.org/10.1108/S0731-905320140000034010
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