Generalized Laplace Inference in Multiple Change-Points Models

Alessandro Casini () and Pierre Perron
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Alessandro Casini: University of Rome Tor Vergata

No WP2020-015, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics

Abstract: Under the classical long-span asymptotic framework we develop a class of Generalized Laplace (GL) inference methods for the change-point dates in a linear time series regression model with multiple structural changes analyzed in, e.g., Bai and Perron (1998). The GL estimator is defined by an integration rather than optimization-based method and relies on the least-squares criterion function. It is interpreted as a classical (non-Bayesian) estimator and the inference methods proposed retain a frequentist interpretation. This approach provides a better approximation about the uncertainty in the data of the change-points relative to existing methods. On the theoretical side, depending on some input (smoothing) parameter, the class of GL estimators exhibits a dual limiting distribution; namely, the classical shrinkage asymptotic distribution, or a Bayes-type asymptotic distribution. We propose an inference method based on Highest Density Regions using the latter distribution. We show that it has attractive theoretical properties not shared by the other popular alternatives, i.e., it is bet-proof. Simulations confirm that these theoretical properties translate to better finite-sample performance.

Keywords: Asymptotic Distribution; Bet-Proof; Break Date; Change-point; Generalized Laplace Inference; Highest Density Region; Quasi-Bayes (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
Pages: 63 pages
Date: 2020-03
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Related works:
Journal Article: GENERALIZED LAPLACE INFERENCE IN MULTIPLE CHANGE-POINTS MODELS (2022)
Working Paper: Generalized Laplace Inference in Multiple Change-Points Models (2021)
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