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Article: Threshold Regression with Endogeneity

TitleThreshold Regression with Endogeneity
Authors
Issue Date2018
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jeconom
Citation
Journal of Econometrics, 2018, v. 203 n. 1, p. 50-68 How to Cite?
AbstractThis paper studies estimation in threshold regression with endogeneity in the regressors and thresholding variable. Three key results differ from those in regular models. First, both the threshold point and the threshold effect parameters are shown to be identified without the need for instrumentation. Second, in partially linear threshold models, both parametric and nonparametric components rely on the same data, which prima facie suggests identification failure. But, as shown here, the discontinuity structure of the threshold itself supplies identifying information for the parametric coefficients without the need for extra randomness in the regressors. Third, instrumentation plays different roles in the estimation of the system parameters, delivering identification for the structural coefficients in the usual way, but raising convergence rates for the threshold effect parameters and improving efficiency for the threshold point. Simulation studies corroborate the theory and the asymptotics. An empirical application is conducted to explore the effects of 401(k) retirement programs on savings, illustrating the relevance of threshold models in treatment effects evaluation in the presence of endogeneity.
Persistent Identifierhttp://hdl.handle.net/10722/243217
ISSN
2017 Impact Factor: 1.632
2015 SCImago Journal Rankings: 3.781
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYu, P-
dc.contributor.authorPhillips, P-
dc.date.accessioned2017-08-25T02:51:46Z-
dc.date.available2017-08-25T02:51:46Z-
dc.date.issued2018-
dc.identifier.citationJournal of Econometrics, 2018, v. 203 n. 1, p. 50-68-
dc.identifier.issn0304-4076-
dc.identifier.urihttp://hdl.handle.net/10722/243217-
dc.description.abstractThis paper studies estimation in threshold regression with endogeneity in the regressors and thresholding variable. Three key results differ from those in regular models. First, both the threshold point and the threshold effect parameters are shown to be identified without the need for instrumentation. Second, in partially linear threshold models, both parametric and nonparametric components rely on the same data, which prima facie suggests identification failure. But, as shown here, the discontinuity structure of the threshold itself supplies identifying information for the parametric coefficients without the need for extra randomness in the regressors. Third, instrumentation plays different roles in the estimation of the system parameters, delivering identification for the structural coefficients in the usual way, but raising convergence rates for the threshold effect parameters and improving efficiency for the threshold point. Simulation studies corroborate the theory and the asymptotics. An empirical application is conducted to explore the effects of 401(k) retirement programs on savings, illustrating the relevance of threshold models in treatment effects evaluation in the presence of endogeneity.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jeconom-
dc.relation.ispartofJournal of Econometrics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleThreshold Regression with Endogeneity-
dc.typeArticle-
dc.identifier.emailYu, P: pingyu@hku.hk-
dc.identifier.authorityYu, P=rp01941-
dc.description.naturepreprint-
dc.identifier.doi10.1016/j.jeconom.2017.09.007-
dc.identifier.hkuros274886-
dc.identifier.volume203-
dc.identifier.issue1-
dc.identifier.spage50-
dc.identifier.epage68-
dc.identifier.isiWOS:000426023100004-
dc.publisher.placeNetherlands-

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