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Estimating Bayesian Decision Problems with Heterogeneous Expertise
Author(s) -
Hansen Stephen,
McMahon Michael,
Srisuma Sorawoot
Publication year - 2015
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2446
Subject(s) - voting , estimator , supreme court , covariate , bayesian probability , econometrics , computer science , order (exchange) , estimation , variation (astronomy) , majority rule , economics , statistics , mathematics , artificial intelligence , law , political science , management , physics , finance , politics , astrophysics
Summary We consider the recent novel two‐step estimator of Iaryczower and Shum ( American Economic Review 2012; 102 : 202–237), who analyze voting decisions of US Supreme Court justices. Motivated by the underlying theoretical voting model, we suggest that where the data under consideration display variation in the common prior, estimates of the structural parameters based on their methodology should generally benefit from including interaction terms between individual and time covariates in the first stage whenever there is individual heterogeneity in expertise. We show numerically, via simulation and re‐estimation of the US Supreme Court data, that the first‐order interaction effects that appear in the theoretical model can have an important empirical implication. Copyright © 2015 John Wiley & Sons, Ltd.