M-Estimation of a Nonparametric Threshold Regression Model

Daniel Henderson (), Christopher Parmeter and Liangjun Su ()

No 2017-15, Working Papers from University of Miami, Department of Economics

Abstract: The present work uses semiparametric M-estimation to construct an estimator for a threshold parameter in a nonparametric regression model. Given that this parameter is only weakly identified, we develop a set of sufficient conditions whereby our semiparametric M-estimator is consistent and asymptotically normal. Our work extends the theory of Chen, Linton and Van Keilegom (2003) to settings where there is weak identification for a semiparametric model. A range of Monte Carlo simulations and three empirical examples (threshold, asymmetric time series and regression discontinuity) support the asymptotic developments.

Keywords: Change Point; M-Estimation; Nonparametric Threshold Regression; Regression Discontinuity; Structural Change Publication Status: Submitted (search for similar items in EconPapers)
JEL-codes: G14 G24 (search for similar items in EconPapers)
Date: 2017-10-19
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.herbert.miami.edu/_assets/files/repec/WP2017-15.pdf First version, 2017 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mia:wpaper:2017-15

Access Statistics for this paper

More papers in Working Papers from University of Miami, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Daniela Valdivia ().