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Association: Name: The Midwest Political Science Association URL: http://www.indiana.edu/~mpsa/
Citation: URL: http://www.allacademic.com/meta/p84518_index.html Direct Link: HTML Code:

Shor, Boris. "A Bayesian Multilevel Modeling Approach to Time Series Cross-Sectional Data" Paper presented at the annual meeting of the The Midwest Political Science Association, Palmer House Hilton, Chicago, Illinois, Apr 07, 2005 <Not Available>. 2008-10-10 <http://www.allacademic.com/meta/p84518_index.html>

Shor, B. , 2005-04-07 "A Bayesian Multilevel Modeling Approach to Time Series Cross-Sectional Data" Paper presented at the annual meeting of the The Midwest Political Science Association, Palmer House Hilton, Chicago, Illinois Online <.PDF>. 2008-10-10 from http://www.allacademic.com/meta/p84518_index.html

Publication Type: Conference Paper/Unpublished Manuscript Review Method: Peer Reviewed Abstract: The analysis of time series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models. However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model (BML) for TSCS data against popular models used in the literature. We use various diagnostics to analyze the performance of our approach relative to these techniques. Compared to the most commonly employed estimators for such data, we find that the Bayesian multilevel model is (1) equally unbiased on average, (2) considerably more efficient, and (3) reports higher quality standard errors. Moreover, the BML is more general and flexible, which offers researchers additional advantages for TSCS data.

Age-Period-Cohort Analyses of Repeated Cross-section Survey Data: A Bayesian Hierarchical Modeling Approach

Estimating Party Effects on Legislative Behavior: Bayesian Estimates Based on European Parliament Data

Multilevel Modelling of Time Series Data