Estimating Bayesian decision problems with heterogeneous priors 2010-2015

DOI

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.

The data is from Iaryczower and Shum (2012), containing the vote (31 in total) of every US Supreme Court justice on every case from 1953-2008. The data court data is henceforth used to estimate parameter that are in turn used to simulate new court data according to the theory highlighted in the description.

Identifier
DOI https://doi.org/10.5255/UKDA-SN-854127
Metadata Access https://datacatalogue.cessda.eu/oai-pmh/v0/oai?verb=GetRecord&metadataPrefix=oai_ddi25&identifier=4a9b6b3400834d9167fd49e79b0c4dc3c66d080f9d52062c5f5f59c09dee3474
Provenance
Creator Hansen, S, Imperial College London; McMahon, M, University of Oxford
Publisher UK Data Service
Publication Year 2020
Funding Reference Economic and Social Research Council
Rights Stephen Hansen, Imperial College London. Michael McMahon, University of Oxford; The Data Collection is available from an external repository. Access is available via Related Resources.
OpenAccess true
Representation
Resource Type Numeric
Discipline Economics; Jurisprudence; Law; Social and Behavioural Sciences
Spatial Coverage United States