Replication Data for: Change-point Detection and Regularization in Time Series Cross Sectional Data Analysis
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. 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 bot' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Changepoint","time series cross sectional"]} <|eot_id|>
 Here is the Description of a dataset. 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 bot' . Please return only a json list of at most 3 Keywords corresponding to the 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 bot
Changepoint; time series cross sectional