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. 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 language | English (US) |
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Pages (from-to) | 816-842 |
Number of pages | 27 |
Journal | Journal of Econometrics |
Volume | 235 |
Issue number | 2 |
DOIs | |
State | Published - Aug 2023 |
Keywords
- Metropolitan area boundary
- Nonparametric
- Random field
- Sample splitting
- Threshold regression
- Tipping point
ASJC Scopus subject areas
- Applied Mathematics
- Economics and Econometrics