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The 50 American States in Space and Time: Applying Conditionally Autoregressive Models

  • Joshua L. Jackson (a1) and James E. Monogan (a1)

Spatial conditionally autoregressive (CAR) models in a hierarchical Bayesian framework can be informative for understanding state politics, or any similar population of border-defined observations. This article explains how a hierarchical CAR model is specified and estimated and then uses Monte Carlo analyses to show when the CAR model offers efficiency gains. We apply this model to data structures common to state politics: A cross-sectional example replicates Erikson, Wright and McIver’s (1993) Statehouse Democracy model and a multilevel panel model example replicates Margalit’s (2013) study of social welfare policy preferences. The CAR model fits better in each case and some inferences differ from models that ignore geographic correlation.

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Previous versions of this paper were presented at the 2010 Annual Summer Meeting of the Society for Political Methodology in Iowa City, IA, the 2011 State Politics and Policy Conference in Hanover, NH, the 2011 Annual Meeting of the American Political Science Association in Seattle, the 2014 Southeast Methods Meeting in Columbia, SC, the Department of Politics and International Relations at the University of Warwick, the Department of Politics at the University of Exeter, the School of Social and Political Science at the University of Glasgow, and the Studying Politics in Time and Space Conference in College Station, TX. Complete replication information, which users are welcome to adapt to their own applications, is available at:

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Political Science Research and Methods
  • ISSN: 2049-8470
  • EISSN: 2049-8489
  • URL: /core/journals/political-science-research-and-methods
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Supplementary materials

Jackson and Monogan dataset

Supplementary materials

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