A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data

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 (MLM). 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 for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.

[1]  Larry M. Bartels.  Pooling Disparate Observations , 1996 .

[2]  Simon Jackman,et al.  Bayesian Inference for Comparative Research , 1994, American Political Science Review.

[3]  Nathaniel Beck,et al.  Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models , 1996, Political Analysis.

[4]  M. Arellano,et al.  Computing Robust Standard Errors for Within-Groups Estimators , 2009 .

[5]  Joseph Hilbe,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2009 .

[6]  S. Jackman.  BAYESIAN ANALYSIS FOR POLITICAL RESEARCH , 2004 .

[7]  Noah Kaplan,et al.  Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation , 2005, Political Analysis.

[8]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models: Fitting multilevel models , 2006 .

[9]  Jonathan N. Katz,et al.  Time-Series-Cross-Section Issues: Dynamics, 2004 , 2004 .

[10]  Larry M. Bartels,et al.  Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable ? , 2004 .

[11]  Bradford S. Jones,et al.  Modeling Multilevel Data Structures , 2002 .

[12]  Simon Jackman,et al.  Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation , 2000, Political Analysis.

[13]  James A. Stimson.  Regression in Space and Time: A Statistical Essay , 1985 .

[14]  Jonathan N. Katz,et al.  What To Do (and Not to Do) with Time-Series Cross-Section Data , 1995, American Political Science Review.

[15]  Andrew Gelman,et al.  Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls , 2004, Political Analysis.

[16]  Jeff Gill,et al.  Elicited Priors for Bayesian Model Specifications in Political Science Research , 2005 .

[17]  William J. Browne,et al.  Bayesian and likelihood-based methods in multilevel modeling 1 A comparison of Bayesian and likelihood-based methods for fitting multilevel models , 2006 .

[18]  A. Brix.  Bayesian Data Analysis, 2nd edn , 2005 .

[19]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[20]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .

[21]  Bruce Western,et al.  Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modeling Approach , 1998 .

[22]  Jonathan N. Katz,et al.  Random Coefficient Models for Time-Series—Cross-Section Data: Monte Carlo Experiments , 2004, Political Analysis.

[23]  Andrew Gelman,et al.  Multilevel (Hierarchical) Modeling: What It Can and Cannot Do , 2006, Technometrics.

[24]  Andrew Gelman,et al.  Fitting Multilevel Models When Predictors and Group Effects Correlate , 2007 .

[25]  Nathaniel N. Beck.  Have We Learned in the Past Few Years , 2001 .

[26]  A. Gelman,et al.  Rich State, Poor State, Red State, Blue State: What's the Matter with Connecticut? , 2005 .