Linking Frequentist and Bayesian Change-Point Methods

被引:0
|
作者
Ardia, David [1 ,2 ]
Dufays, Arnaud [3 ]
Ordas Criado, Carlos [4 ]
机构
[1] HEC Montreal, GERAD, Montreal, PQ, Canada
[2] HEC Montreal, Dept Decis Sci, Montreal, PQ, Canada
[3] EDHEC Business Sch, Fac Data Sci Econ & Finance, Roubaix, France
[4] Laval Univ, Dept Econ, Quebec City, PQ, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Change-point; Minimum description length; Model selection/combination; Structural change; MULTIPLE CHANGE-POINT; MODEL SELECTION; BINARY SEGMENTATION; INFERENCE; CRITERIA; BREAKS;
D O I
10.1080/07350015.2023.2293166
中图分类号
F [经济];
学科分类号
02 ;
摘要
We show that the two-stage minimum description length (MDL) criterion widely used to estimate linear change-point (CP) models corresponds to the marginal likelihood of a Bayesian model with a specific class of prior distributions. This allows results from the frequentist and Bayesian paradigms to be bridged together. Thanks to this link, one can rely on the consistency of the number and locations of the estimated CPs and the computational efficiency of frequentist methods, and obtain a probability of observing a CP at a given time, compute model posterior probabilities, and select or combine CP methods via Bayesian posteriors. Furthermore, we adapt several CP methods to take advantage of the MDL probabilistic representation. Based on simulated data, we show that the adapted CP methods can improve structural break detection compared to state-of-the-art approaches. Finally, we empirically illustrate the usefulness of combining CP detection methods when dealing with long time series and forecasting.
引用
收藏
页码:1155 / 1168
页数:14
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