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dc.contributor.authorLi, Yeen_US
dc.contributor.authorPerron, Pierreen_US
dc.date.accessioned2018-01-23T19:07:45Z
dc.date.available2018-01-23T19:07:45Z
dc.date.issued2017-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000385938400015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationYe Li, Pierre Perron. 2017. "Inference on locally ordered breaks in multiple regressions." ECONOMETRIC REVIEWS, Volume 36, Issue 1-3, pp. 289 - 353 (65).
dc.identifier.issn0747-4938
dc.identifier.issn1532-4168
dc.identifier.urihttps://hdl.handle.net/2144/26274
dc.description.abstractWe consider issues related to inference about locally ordered breaks in a system of equations, as originally proposed by Qu and Perron (2007 Qu, Z., Perron, P. (2007). Estimating and testing structural changes in multivariate regressions. Econometrica 75:459–502.[Crossref], [Web of Science ®], [Google Scholar]). These apply when break dates in different equations within the system are not separated by a positive fraction of the sample size. This allows constructing joint confidence intervals of all such locally ordered break dates. We extend the results of Qu and Perron (2007 Qu, Z., Perron, P. (2007). Estimating and testing structural changes in multivariate regressions. Econometrica 75:459–502.[Crossref], [Web of Science ®], [Google Scholar]) in several directions. First, we allow the covariates to be any mix of trends and stationary or integrated regressors. Second, we allow for breaks in the variance-covariance matrix of the errors. Third, we allow for multiple locally ordered breaks, each occurring in a different equation within a subset of equations in the system. Via some simulation experiments, we show first that the limit distributions derived provide good approximations to the finite sample distributions. Second, we show that forming confidence intervals in such a joint fashion allows more precision (tighter intervals) compared to the standard approach of forming confidence intervals using the method of Bai and Perron (1998 Bai, J., Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica 66:47–78.[Crossref], [Web of Science ®], [Google Scholar]) applied to a single equation. Simulations also indicate that using the locally ordered break confidence intervals yields better coverage rates than using the framework for globally distinct breaks when the break dates are separated by roughly 10% of the total sample size.en_US
dc.format.extentp. 289 - 353en_US
dc.languageEnglish
dc.publisherTAYLOR & FRANCIS INCen_US
dc.relation.ispartofECONOMETRIC REVIEWS
dc.subjectSocial sciencesen_US
dc.subjectScience & technologyen_US
dc.subjectPhysical sciencesen_US
dc.subjectEconomicsen_US
dc.subjectMathematics, interdisciplinary applicationsen_US
dc.subjectSocial sciences, mathematical methodsen_US
dc.subjectStatistics & probabilityen_US
dc.subjectBusiness & economicsen_US
dc.subjectMathematicsen_US
dc.subjectMathematical methods in social sciencesen_US
dc.subjectBreak datesen_US
dc.subjectChange-pointsen_US
dc.subjectLocally ordered breaksen_US
dc.subjectMultiple regressionsen_US
dc.subjectTime-seriesen_US
dc.subjectEconometricsen_US
dc.titleInference on locally ordered breaks in multiple regressionsen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/07474938.2015.1114552
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Economicsen_US
pubs.publication-statusPublisheden_US


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