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Activity Number: 312
Type: Invited
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Council of Chapters
Abstract - #303753
Title: Identification, Learning, and Less Informative Priors in Endogenous Variable Models
Author(s): Justin L Tobias*+ and Josh Chan
Companies: Purdue University and Australian National University
Address: , Lafayette, IN, 47907, USA
Keywords: Bayes ; Endogeneity ; Identification ; MCMC
Abstract:

Estimation in models with endogeneity concerns begins by searching for instruments. This search is inherently subjective and formal identification of such models is generally achieved upon imposing the researcher's strong prior belief that such variables have no conditional impacts on the outcome variable. Results obtained from such analyses are necessarily conditioned upon the untestable opinions of the researcher, and such beliefs may not be widely shared.

In this paper we employ another Bayesian approach to estimation and inference in models with endogeneity concerns by imposing weaker prior assumptions than the dogmatic priors that are commonly used to exclude variables from the model. Doing so, however, requires working with a partially identified model. We introduce our prior, compare it to related methods introduced in recent work, and describe strategies for calculating marginal posteriors of interest. Our methods are also applied with generated data and in an application involving the impact of the Body Mass Index (BMI) on earnings


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