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Article: Likelihood estimation and inference in threshold regression
Title | Likelihood estimation and inference in threshold regression |
---|---|
Authors | |
Keywords | Bayes Boundary Compound Poisson process Credible set Efficiency bounds Local asymptotic minimax Middle-point MLE Nonregular models Structural change Threshold regression WienerHopf equation |
Issue Date | 2012 |
Citation | Journal of Econometrics, 2012, v. 167, n. 1, p. 274-294 How to Cite? |
Abstract | This paper studies likelihood-based estimation and inference in parametric discontinuous threshold regression models with i.i.d. data. The setup allows heteroskedasticity and threshold effects in both mean and variance. By interpreting the threshold point as a "middle" boundary of the threshold variable, we find that the Bayes estimator is asymptotically efficient among all estimators in the locally asymptotically minimax sense. In particular, the Bayes estimator of the threshold point is asymptotically strictly more efficient than the left-endpoint maximum likelihood estimator and the newly proposed middle-point maximum likelihood estimator. Algorithms are developed to calculate asymptotic distributions and risk for the estimators of the threshold point. The posterior interval is proved to be an asymptotically valid confidence interval and is attractive in both length and coverage in finite samples. © 2011 Elsevier B.V. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/202149 |
ISSN | 2023 Impact Factor: 9.9 2023 SCImago Journal Rankings: 9.161 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yu, Ping | - |
dc.date.accessioned | 2014-08-22T02:57:43Z | - |
dc.date.available | 2014-08-22T02:57:43Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | Journal of Econometrics, 2012, v. 167, n. 1, p. 274-294 | - |
dc.identifier.issn | 0304-4076 | - |
dc.identifier.uri | http://hdl.handle.net/10722/202149 | - |
dc.description.abstract | This paper studies likelihood-based estimation and inference in parametric discontinuous threshold regression models with i.i.d. data. The setup allows heteroskedasticity and threshold effects in both mean and variance. By interpreting the threshold point as a "middle" boundary of the threshold variable, we find that the Bayes estimator is asymptotically efficient among all estimators in the locally asymptotically minimax sense. In particular, the Bayes estimator of the threshold point is asymptotically strictly more efficient than the left-endpoint maximum likelihood estimator and the newly proposed middle-point maximum likelihood estimator. Algorithms are developed to calculate asymptotic distributions and risk for the estimators of the threshold point. The posterior interval is proved to be an asymptotically valid confidence interval and is attractive in both length and coverage in finite samples. © 2011 Elsevier B.V. All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Econometrics | - |
dc.subject | Bayes | - |
dc.subject | Boundary | - |
dc.subject | Compound Poisson process | - |
dc.subject | Credible set | - |
dc.subject | Efficiency bounds | - |
dc.subject | Local asymptotic minimax | - |
dc.subject | Middle-point MLE | - |
dc.subject | Nonregular models | - |
dc.subject | Structural change | - |
dc.subject | Threshold regression | - |
dc.subject | WienerHopf equation | - |
dc.title | Likelihood estimation and inference in threshold regression | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.jeconom.2011.12.002 | - |
dc.identifier.scopus | eid_2-s2.0-84856362463 | - |
dc.identifier.volume | 167 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 274 | - |
dc.identifier.epage | 294 | - |
dc.identifier.isi | WOS:000300863300017 | - |
dc.identifier.issnl | 0304-4076 | - |