Clustering Time: Applying Bayesian Mixture Models to Estimate Temporally Heterogeneous Effects in Longitudinal Analysis

24 Pages Posted: 27 Aug 2013

See all articles by Paasha Mahdavi

Paasha Mahdavi

University of California Santa Barbara - Dept of Political Science

Antonio Pedro Ramos

University of California, Los Angeles (UCLA)

Date Written: 2013

Abstract

The presence of temporally heterogeneous effects is prevalent in longitudinal (panel) analysis in the social sciences. The effects of predictors on outcomes of interest may vary across time, often following complex patterns. Though unit heterogeneity is more commonly addressed in quantitative studies using longitudinal data, a growing body of literature has begun to directly model the presence of time-varying effects using methods such as time fixed-effects, time interactions, unstructured time models, structural break models, and dynamic linear models. This study considers an alternative approach that allows researchers to answer questions regarding (1) temporally heterogeneous effects and (2) how these effects are clustered over time. Using the debate surrounding the presence of a resource curse (Ross 2012) as an example, we apply a Bayesian mixture modeling (BMM) framework to address time-varying effects of oil wealth on democratic governance. Results indicate that the BMM approach provides evidence for the presence of a resource curse for the periods 1960-1987 and 1995-2003, with null effects in the 1987-1990 period and positive effects in the 1991-1994 era. The advantage to the BMM framework, we argue, is the lack of \textit{ad hoc} temporal modeling which can often lead to high model dependence; instead by using a data-based approach to temporal clustering, we flexibly allow for hypotheses of theoretical interest to be tested against the data rather than be assumed by the model.

Keywords: Longitudinal analysis, temporal heterogeneity, dynamic effects, clusters, Bayes, mixture models, resource curse

Suggested Citation

Mahdavi, Paasha and Ramos, Antonio Pedro, Clustering Time: Applying Bayesian Mixture Models to Estimate Temporally Heterogeneous Effects in Longitudinal Analysis (2013). APSA 2013 Annual Meeting Paper, American Political Science Association 2013 Annual Meeting, Available at SSRN: https://ssrn.com/abstract=2303515

Paasha Mahdavi (Contact Author)

University of California Santa Barbara - Dept of Political Science ( email )

Ellison 3807
Mail Code: 9420
Santa Barbara, CA 93106
United States

HOME PAGE: http://www.paashamahdavi.com

Antonio Pedro Ramos

University of California, Los Angeles (UCLA) ( email )

405 Hilgard Avenue
Box 951361
Los Angeles, CA 90095
United States