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A Permutation-Based Changepoint Technique for Monitoring Effect Sizes

Published online by Cambridge University Press:  05 January 2021

Daniel Kent Open the ORCID record for Daniel Kent [Opens in a new window] ,
James D. Wilson  and
Skyler J. Cranmer
Daniel Kent*
Affiliation:
Department of Political Science, The Ohio State University, 2140 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA. Email: [email protected]
James D. Wilson
Affiliation:
Department of Mathematics and Statistics, University of San Francisco, San Francisco, CA 94117, USA. Email: [email protected]
Skyler J. Cranmer
Affiliation:
Department of Political Science, The Ohio State University, 2140 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA. Email: [email protected]
*
Corresponding author Daniel Kent
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Abstract

Across the social sciences, scholars regularly pool effects over substantial periods of time, a practice that produces faulty inferences if the underlying data generating process is dynamic. To help researchers better perform principled analyses of time-varying processes, we develop a two-stage procedure based upon techniques for permutation testing and statistical process monitoring. Given time series cross-sectional data, we break the role of time through permutation inference and produce a null distribution that reflects a time-invariant data generating process. The null distribution then serves as a stable reference point, enabling the detection of effect changepoints. In Monte Carlo simulations, our randomization technique outperforms alternatives for changepoint analysis. A particular benefit of our method is that, by establishing the bounds for time-invariant effects before interacting with actual estimates, it is able to differentiate stochastic fluctuations from genuine changes. We demonstrate the method’s utility by applying it to a popular study on the relationship between alliances and the initiation of militarized interstate disputes. The example illustrates how the technique can help researchers make inferences about where changes occur in dynamic relationships and ask important questions about such changes.

Keywords

changepoint analysispermutation teststime series cross-sectional data
Type
Article
Information
Political Analysis , Volume 30 , Issue 2 , April 2022 , pp. 167 - 178
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Sunshine Hillygus

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