Continuous record Laplace-based inference about the break date in structural change models

Alessandro Casini and Pierre Perron

Journal of Econometrics, 2021, vol. 224, issue 1, 3-21

Abstract: Building upon the continuous record asymptotic framework recently introduced by Casini and Perron (2020a) for inference in structural change models, we propose a Laplace-based (Quasi-Bayes) procedure for the construction of the estimate and confidence set for the date of a structural change. It is defined by an integration rather than an optimization-based method. A transformation of the least-squares criterion function is evaluated in order to derive a proper distribution, referred to as the Quasi-posterior. For a given choice of a loss function, the Laplace-type estimator is the minimizer of the expected risk with the expectation taken under the Quasi-posterior. Besides providing an alternative estimate that is more precise—lower mean absolute error (MAE) and lower root-mean squared error (RMSE)—than the usual least-squares one, the Quasi-posterior distribution can be used to construct asymptotically valid inference using the concept of Highest Density Region. The resulting Laplace-based inferential procedure is shown to have lower MAE and RMSE, and the confidence sets strike a better balance between empirical coverage rates and average lengths of the confidence sets relative to traditional long-span methods, whether the break size is small or large.

Keywords: Asymptotic distribution; Bias; Break date; Change-point; Generalized Laplace; Infill asymptotics; Semimartingale (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Related works:
Working Paper: Continuous Record Laplace-based Inference about the Break Date in Structural Change Models (2020)
Working Paper: Continuous Record Laplace-based Inference about the Break Date in Structural Change Models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:224:y:2021:i:1:p:3-21

DOI: 10.1016/j.jeconom.2020.05.020

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