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.

Additional Metadata
Keywords Markov models, cross section analysis
Persistent URL dx.doi.org/10.1093/pan/10.2.113, hdl.handle.net/1765/13531
Journal Political Analysis
Citation
Pelzer, B, Eisinga, R, & Franses, Ph.H.B.F. (2002). Inferring transition probabilities from repeated cross sections: A cross-level inference approach to US presidential voting. Political Analysis, 113–133. doi:10.1093/pan/10.2.113