Threshold Regression with Nonparametric Sample Splitting

Yoonseok Lee and Yulong Wang

Papers from arXiv.org

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.

Date: 2019-05, Revised 2021-01
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Journal Article: Threshold regression with nonparametric sample splitting (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1905.13140

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