2022/01/22 Statistics & Time Series for Policy Intervention & Change Identification, Professor Patrick Brandt

Title:  Changepoint Models: Statistics and Time Series for Policy Intervention and Change Identification
題目： 變更點模型：政策干預和變更識別的統計和時間序列
 
Date: November 17, 2022 [US Central time] | November 18, 2022 [Taiwan]
Time: 21:00 - 23:00 [US Central time] | 10:00 - 12:00 [Taiwan]
Speaker: Dr. Patrick Brandt
 
Abstract:
How does one identify and find policy and regime changes over time? Here we review data-drive techniques for the statistical detection and identification of data changes over time and provide a taxonomy for how to diagnose and think about these methods.
摘要：
人們如何識別和發現政策和製度隨時間的變化？在這裡，我們回顧了用於統計檢測和識別數據隨時間變化的數據驅動技術，並提供瞭如何診斷和思考這些方法的分類法。
 
 
Dr. Patrick Brandt
Patrick T. Brandt is Professor of Public Policy, and Political Economy, and Political Science in the School of Economic, Political and Policy Sciences at the University of Texas at Dallas. His research employs time series analysis methods and machine learning in a variety of areas. The main time series models employed in this research involve Bayesian statistics, multiple equation or vector autoregression models, methods for producing and evaluating the quality of forecasts, the derivation of new models for time series of counts, and modeling structural change and endogenous shifts in data over time.
 
主講人簡介：
Patrick T. Brandt 是德克薩斯大學達拉斯分校經濟、政治和政策科學學院的公共政策、政治經濟學和政治學教授。他的研究在多個領域採用時間序列分析方法和機器學習。本研究中使用的主要時間序列模型涉及貝葉斯統計、多方程或向量自回歸模型、預測質量的生成和評估方法、計數時間序列新模型的推導，以及數據結構變化和內生變化的建模隨著時間的推移。