Estimating Bayesian decision problems with heterogeneous priors 2010-2015 - Hansen, Stephen (UK Data Service, 2020)
The files included in this project are therefore the US Supreme court data that is obtained from Iaryczower and Shum (2012). It contains the vote of every justice (31 in total) on every case from 1953-2008. The files also include the R code that is used to Simulate the re-estimate the court data. The project considers the novel two-step estimator of Iaryczower and Shum (2012), who analyze voting decisions of US Supreme Court justices. Motivated by the underlying theoretical voting model, it suggests that where the data under consideration displays 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. It shows 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.