The present work describes a simple approach to estimating the location of a threshold/changepoint in a nonparametric regression. This model has connections both to the time-series and regressiondiscontinuity literatures. The estimator leverages a simple decomposition, giving it the form of asemiparametric smooth coefficient model. Optimal bandwidth selection and a suite of testing facilitiesare also presented. Several empirical examples are provided to illustrate the implementation of themethods discussed here.
Change Point, Local Average Treatment Effect, Nonparametric Threshold Regression, Regression Discontinuity, Smoothed Bootstrap, Structural Change
HENDERSON, Daniel J.; PARMETER, Christopher F.; and SU, Liangjun.
Nonparametric threshold regression: Estimation and inference. (2015). 1-62. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/2066
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.