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The GL estimator is defined by an integration rather than optimization-based method and relies on the LS 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 good finite-sample performance.<\/jats:p>","DOI":"10.1017\/s0266466621000013","type":"journal-article","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T09:50:51Z","timestamp":1614073851000},"page":"35-65","source":"Crossref","is-referenced-by-count":13,"title":["GENERALIZED LAPLACE INFERENCE IN MULTIPLE CHANGE-POINTS MODELS"],"prefix":"10.1017","volume":"38","author":[{"given":"Alessandro","family":"Casini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Perron","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2021,2,23]]},"reference":[{"key":"S0266466621000013_r63","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176342362"},{"key":"S0266466621000013_r65","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-4145-2"},{"key":"S0266466621000013_r11","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177706070"},{"key":"S0266466621000013_r57","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2005.06.030"},{"key":"S0266466621000013_r10","volume-title":"Bayesian Inference in Bayesian Analysis","author":"Box","year":"1973"},{"key":"S0266466621000013_r39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2018.03.009"},{"key":"S0266466621000013_r64","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0262.2006.00754.x"},{"key":"S0266466621000013_r36","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176349143"},{"key":"S0266466621000013_r66","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1111\/j.2517-6161.1977.tb01638.x","article-title":"Conservative statistical inference","volume":"39","author":"Robinson","year":"1977","journal-title":"Journal of the Royal Statistical Society Series B"},{"key":"S0266466621000013_r47","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/76.3.409"},{"key":"S0266466621000013_r9","doi-asserted-by":"publisher","DOI":"10.1007\/BF00531650"},{"key":"S0266466621000013_r4","doi-asserted-by":"publisher","DOI":"10.1017\/S026646660000935X"},{"key":"S0266466621000013_r7","doi-asserted-by":"publisher","DOI":"10.1002\/jae.659"},{"key":"S0266466621000013_r1","doi-asserted-by":"publisher","DOI":"10.2307\/2951764"},{"key":"S0266466621000013_r49","doi-asserted-by":"publisher","DOI":"10.1111\/jtsa.12179"},{"key":"S0266466621000013_r68","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4076(98)00034-7"},{"key":"S0266466621000013_r30","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1034713651"},{"key":"S0266466621000013_r24","volume-title":"Limit Theorems in Change-Point Analysis","author":"Cs\u00f6rg\u0151","year":"1997"},{"key":"S0266466621000013_r26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2007.02.001"},{"key":"S0266466621000013_r61","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2004.09.004"},{"key":"S0266466621000013_r14","unstructured":"Casini, A. , Deng, T. , & Perron, P. 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