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Replication Data for: Change-point Detection and Regularization in Time Series Cross Sectional Data Analysis

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Change-point Detection and Regularization in Time Series Cross Sectional Data Analysis
 
Identifier https://doi.org/10.7910/DVN/MCQTYC
 
Creator Park, Jong Hee
Yamauchi, Soichiro
 
Publisher Harvard Dataverse
 
Description Researchers of time series cross sectional (TSCS) data regularly face the change-point problem, which re- quires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high dimensional data and present its application in the context of the fixed-effect model. Our proposed method, hidden Markov Bayesian bridge model (HMBB), jointly estimates high dimensional regime-specific parameters and hidden regime transitions in a unified way. We apply our method to Alvarez, Garrett, and Lange (1991)’s study of the relationship between government partisanship and economic growth and Allee and Scalera (2012)’s study of membership effects in international organizations. In both applications, we found that the proposed method successfully identify substantively meaningful temporal heterogeneity in parameters of regression models.
 
Subject Social Sciences
Changepoint
time series cross sectional
 
Contributor Jong Hee Park