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Part 1: Document Description
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Citation |
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Title: |
Replication data for: Inferring Transition Probabilities from Repeated Cross Sections |
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Identification Number: |
doi:10.7910/DVN/MKJ5EN |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2010-02-18 |
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Version: |
1 |
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Bibliographic Citation: |
Ben Pelzer; Rob Eisinga; Philip Hans Franses, 2010, "Replication data for: Inferring Transition Probabilities from Repeated Cross Sections", https://doi.org/10.7910/DVN/MKJ5EN, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication data for: Inferring Transition Probabilities from Repeated Cross Sections |
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Identification Number: |
doi:10.7910/DVN/MKJ5EN |
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Authoring Entity: |
Ben Pelzer (University of Nijmegen) |
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Rob Eisinga (University of Nijmegen) |
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Philip Hans Franses (Erasmus University Rotterdam) |
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Producer: |
Political Analysis |
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Date of Production: |
2002 |
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Distributor: |
Harvard Dataverse |
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Distributor: |
Murray Research Archive |
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Date of Deposit: |
2010-02-18 |
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Holdings Information: |
https://doi.org/10.7910/DVN/MKJ5EN |
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Study Scope |
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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. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Ben Pelzer, Rob Eisinga, and Philip Hans Franses. 2002. "Inferring Transition Probabilities from Repeated Cross Sections." Political Analysis, 10(2) 113-133. <a href= "http://pan.oxfordjournals.org/cgi/reprint/10/2/113" target= "_new">subscribe to Political Analysis to access the full article and supplementary data</a> |
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Bibliographic Citation: |
Ben Pelzer, Rob Eisinga, and Philip Hans Franses. 2002. "Inferring Transition Probabilities from Repeated Cross Sections." Political Analysis, 10(2) 113-133. <a href= "http://pan.oxfordjournals.org/cgi/reprint/10/2/113" target= "_new">subscribe to Political Analysis to access the full article and supplementary data</a> |