Threshold regression with nonparametric sample splitting

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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. We illustrate empirical relevance of this new model 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.

Original languageEnglish (US)
Pages (from-to)816-842
Number of pages27
JournalJournal of Econometrics
Issue number2
StatePublished - Aug 2023


  • Metropolitan area boundary
  • Nonparametric
  • Random field
  • Sample splitting
  • Threshold regression
  • Tipping point

ASJC Scopus subject areas

  • Applied Mathematics
  • Economics and Econometrics


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