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Random Coefficient Models for Time-Series–Cross-Section Data

Beck, Nathaniel and Katz, Jonathan N. (2004) Random Coefficient Models for Time-Series–Cross-Section Data. Social Science Working Paper, 1205. California Institute of Technology , Pasadena, CA. (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20191018-164513923

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Abstract

This paper considers random coefficient models (RCMs) for time-series–cross-section data. These models allow for unit to unit variation in the model parameters. After laying out the various models, we assess several issues in specifying RCMs. We then consider the finite sample properties of some standard RCM estimators, and show that the most common one, associated with Hsiao, has very poor properties. These analyses also show that a somewhat awkward combination of estimators based on Swamy’s work performs reasonably well; this awkward estimator and a Bayes estimator with an uninformative prior (due to Smith) seem to perform best. But we also see that estimators which assume full pooling perform well unless there is a large degree of unit to unit parameter heterogeneity. We also argue that the various data driven methods (whether classical or empirical Bayes or Bayes with gentle priors) tends to lead to much more heterogeneity than most political scientists would like. We speculate that fully Bayesian models, with a variety of informative priors, may be the best way to approach RCMs.


Item Type:Report or Paper (Working Paper)
ORCID:
AuthorORCID
Katz, Jonathan N.0000-0002-5287-3503
Additional Information:We gratefully acknowledge the financial support of the National Science Foundation. We are thankful to Larry Bartels for always reminding us that our judgment may outperform the data. Lastly, we thank Geoffrey Garrett for allowing us to use his data.
Group:Social Science Working Papers
Funders:
Funding AgencyGrant Number
NSFUNSPECIFIED
Series Name:Social Science Working Paper
Issue or Number:1205
Record Number:CaltechAUTHORS:20191018-164513923
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191018-164513923
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99379
Collection:CaltechAUTHORS
Deposited By: Katherine Johnson
Deposited On:18 Oct 2019 23:54
Last Modified:18 Oct 2019 23:54

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