M-Estimation of a Nonparametric Threshold Regression Model
Daniel Henderson (),
Christopher Parmeter and
No 2017-15, Working Papers from University of Miami, Department of Economics
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)
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-sea
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Persistent link: https://EconPapers.repec.org/RePEc:mia:wpaper:2017-15
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