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- W2783804064 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." @default.
- W2783804064 created "2018-01-26" @default.
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- W2783804064 date "2017-10-19" @default.
- W2783804064 modified "2023-09-27" @default.
- W2783804064 title "M-Estimation of a Nonparametric Threshold Regression Model" @default.
- W2783804064 hasPublicationYear "2017" @default.
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