Linking Frequentist and Bayesian Change-Point Methods

David Ardia, Arnaud Dufays and Carlos Ordás Criado ()

MPRA Paper from University Library of Munich, Germany

Abstract: 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.

Keywords: Change-point; Minimum description length; Model selection/combination; Structural change. (search for similar items in EconPapers)
JEL-codes: C11 C12 C22 C32 C52 C53 (search for similar items in EconPapers)
Date: 2023-12-20
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:119486

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