Change-Point Detection and Regularization in Time Series Cross-Sectional Data Analysis

Researchers of time series cross-sectional data regularly face the change-point problem, which requires 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, jointly estimates high-dimensional regime-specific parameters and hidden regime transitions in a unified way. We apply our method to Alvarez, Garrett, and Lange's (1991, American Political Science Review 85, 539-556) study of the relationship between government partisanship and economic growth and Allee and Scalera's (2012, International Organization 66, 243-276) 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.

Automated summary

This paper develops a general Bayesian method for change-point detection in high-dimensional data and applies it in the fixed-effect model. The proposed method can jointly estimate high-dimensional parameters and hidden change-points, successfully identifying temporal heterogeneity in regression model parameters.