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Inferring Transition Probabilities from Repeated Cross Sections

Published online by Cambridge University Press:  04 January 2017

Ben Pelzer ,
Rob Eisinga  and
Philip Hans Franses
Ben Pelzer
Affiliation:
Research Technical Department, University of Nijmegen, P.O. Box 9104, 6500 HE Nijmegen, The Netherlands. e-mail: [email protected]
Rob Eisinga
Affiliation:
Department of Social Science Research Methods, University of Nijmegen, P.O. Box 9104, 6500 HE Nijmegen, The Netherlands. e-mail: [email protected]
Philip Hans Franses
Affiliation:
Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. e-mail: [email protected]
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Abstract

This paper discusses a nonstationary, heterogeneous Markov model designed to estimate entry and exit transition probabilities at the micro level from a time series of independent cross-sectional samples with a binary outcome variable. The model has its origins in the work of Moffitt and shares features with standard statistical methods for ecological inference. We outline the methodological framework proposed by Moffitt and present several extensions of the model to increase its potential application in a wider array of research contexts. We also discuss the relationship with previous lines of related research in political science. The example illustration uses survey data on American presidential vote intentions from a five-wave panel study conducted by Patterson in 1976. We treat the panel data as independent cross sections and compare the estimates of the Markov model with both dynamic panel parameter estimates and the actual observations in the panel. The results suggest that the proposed model provides a useful framework for the analysis of transitions in repeated cross sections. Open problems requiring further study are discussed.

Type
Articles
Information
Political Analysis , Volume 10 , Issue 2 , Spring 2002 , pp. 113 - 133
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2002 

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