Threshold Regression with Nonparametric Sample Splitting
Abstract
This paper develops a threshold regression model where an unknown relationship between two variables nonparametrically determines the threshold. We allow the observations to be cross-sectionally dependent so that the model can be applied to determine an unknown spatial border for sample splitting over a random field. We derive the uniform rate of convergence and the nonstandard limiting distribution of the nonparametric threshold estimator. We also obtain the root-n consistency and the asymptotic normality of the regression coefficient estimator. Our model has broad empirical relevance as illustrated by estimating the tipping point in social segregation problems as a function of demographic characteristics; and determining metropolitan area boundaries using nighttime light intensity collected from satellite imagery. We find that the new empirical results are substantially different from those in the existing studies.
Keywords
Cite
@article{arxiv.1905.13140,
title = {Threshold Regression with Nonparametric Sample Splitting},
author = {Yoonseok Lee and Yulong Wang},
journal= {arXiv preprint arXiv:1905.13140},
year = {2021}
}