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Article: Threshold Regression With A Threshold Boundary

TitleThreshold Regression With A Threshold Boundary
Authors
Keywordsthreshold regression
threshold boundary
Poisson point process
compound Poisson field
two-sided Brownian field
Issue Date2020
PublisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandfonline.com/loi/ubes20
Citation
Journal of Business and Economic Statistics, 2020, Epub 2020-03-10 How to Cite?
AbstractThis paper studies computation, estimation, inference and testing for linearity in threshold regression with a threshold boundary. We first put forward a new algorithm to ease the computation of the threshold boundary, and develop the asymptotics for the least squares estimator in both the fixed-threshold-effect framework and the small-threshold-effect framework. We also show that the inverting-likelihood-ratio method is not suitable to construct confidence sets for the threshold parameters, while the nonparametric posterior interval is still applicable. We then propose a new score-type test to test for the existence of threshold effects. Comparing with the usual Wald-type test, it is computationally less intensive, and its critical values are easier to obtain by the simulation method. Simulation studies corroborate the theoretical results. We finally conduct two empirical applications in labor economics to illustrate the nonconstancy of thresholds.
Persistent Identifierhttp://hdl.handle.net/10722/281852
ISSN
2019 Impact Factor: 2.935
2015 SCImago Journal Rankings: 2.566

 

DC FieldValueLanguage
dc.contributor.authorYu, P-
dc.contributor.authorFan, X-
dc.date.accessioned2020-04-03T07:22:42Z-
dc.date.available2020-04-03T07:22:42Z-
dc.date.issued2020-
dc.identifier.citationJournal of Business and Economic Statistics, 2020, Epub 2020-03-10-
dc.identifier.issn0735-0015-
dc.identifier.urihttp://hdl.handle.net/10722/281852-
dc.description.abstractThis paper studies computation, estimation, inference and testing for linearity in threshold regression with a threshold boundary. We first put forward a new algorithm to ease the computation of the threshold boundary, and develop the asymptotics for the least squares estimator in both the fixed-threshold-effect framework and the small-threshold-effect framework. We also show that the inverting-likelihood-ratio method is not suitable to construct confidence sets for the threshold parameters, while the nonparametric posterior interval is still applicable. We then propose a new score-type test to test for the existence of threshold effects. Comparing with the usual Wald-type test, it is computationally less intensive, and its critical values are easier to obtain by the simulation method. Simulation studies corroborate the theoretical results. We finally conduct two empirical applications in labor economics to illustrate the nonconstancy of thresholds.-
dc.languageeng-
dc.publisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandfonline.com/loi/ubes20-
dc.relation.ispartofJournal of Business and Economic Statistics-
dc.rightsAOM/Preprint Before Accepted: his article has been accepted for publication in [JOURNAL TITLE], published by Taylor & Francis. AOM/Preprint After Accepted: This is an [original manuscript / preprint] of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. Accepted Manuscript (AM) i.e. Postprint This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI].-
dc.subjectthreshold regression-
dc.subjectthreshold boundary-
dc.subjectPoisson point process-
dc.subjectcompound Poisson field-
dc.subjecttwo-sided Brownian field-
dc.titleThreshold Regression With A Threshold Boundary-
dc.typeArticle-
dc.identifier.emailYu, P: pingyu@hku.hk-
dc.identifier.authorityYu, P=rp01941-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/07350015.2020.1740712-
dc.identifier.scopuseid_2-s2.0-85083573407-
dc.identifier.hkuros309662-
dc.identifier.volumeEpub 2020-03-10-
dc.publisher.placeUnited States-

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