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

被引:1
|
作者
Park, Jong Hee [1 ]
Yamauchi, Soichiro [2 ]
机构
[1] Seoul Natl Univ, IR Data Ctr, Dept Polit Sci & Int Relat, Seoul, South Korea
[2] Harvard Univ, Dept Govt, Cambridge, MA 02138 USA
关键词
Bayesian inference; change-point detection; regularization; shrinkage; high-dimensional data; HIGH-DIMENSIONAL REGRESSION; GOVERNMENT PARTISANSHIP; LABOR ORGANIZATION; VARIABLE SELECTION; TRADE; POLICY; HETEROGENEITY; BRIDGE; LASSO; LIKELIHOOD;
D O I
10.1017/pan.2022.23
中图分类号
D0 [政治学、政治理论];
学科分类号
0302 ; 030201 ;
摘要
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.
引用
收藏
页码:257 / 277
页数:21
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