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

Joshua L. Jackson and James E. Monogan

Political Science Research and Methods, 2020, vol. 8, issue 3, 543-557

Abstract: 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.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:pscirm:v:8:y:2020:i:3:p:543-557_10

Access Statistics for this article

More articles in Political Science Research and Methods from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().