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dc.contributor.authorCasini, Alessandroen_US
dc.contributor.authorPerron, Pierreen_US
dc.date.accessioned2020-05-08T14:44:10Z
dc.date.accessioned2020-06-08T18:23:18Z
dc.date.available2020-05-08T14:44:10Z
dc.date.available2020-06-08T18:23:18Z
dc.date.issued2020
dc.identifier.citationAlessandro Casini, Pierre Perron. 2020. "Generalized Laplace inference in multiple change-points models."
dc.identifier.urihttps://hdl.handle.net/2144/41148
dc.description.abstractUnder 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.en_US
dc.language.isoen_US
dc.language.isoen_US
dc.relation.replaceshttps://hdl.handle.net/2144/40701
dc.relation.replaces2144/40701
dc.subjectAsymptotic distributionen_US
dc.subjectBreak dateen_US
dc.subjectChange-pointen_US
dc.subjectGeneralized Laplaceen_US
dc.subjectHighest density regionen_US
dc.subjectQuasi-Bayesen_US
dc.titleGeneralized Laplace inference in multiple change-points modelsen_US
dc.typeArticleen_US
dc.description.versionFirst author draften_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, Administrationen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Economicsen_US
pubs.publication-statusSubmitteden_US
dc.identifier.mycv402917


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