Panel Kink Threshold Regression Model with a Covariate-Dependent Threshold

This paper extends the kink threshold regression (KTR) model with a constant threshold in Hansen (2017) to a panel data framework with a covariate-dependent threshold, where the threshold is modeled as a function of informative covariates. We suggest an estimator based on the within-group transformation, and propose test statistics for kink threshold effect and threshold constancy. We establish the asymptotic joint normality of the slope and threshold estimators, and derive the limiting distributions of the test statistics. Our asymptotical results show that the inclusion of a covariate-dependent threshold does not affect the asymptotic joint normality of the slope and threshold estimates in the kink threshold regression model. Monte Carlo simulations show that the finite sample proprieties of the proposed estimator and test statistics are generally satisfactory.

Monte Carlo Comparison for Nonparametric Threshold Estimators

This paper compares the finite sample performance of three non-parametric threshold estimators via the Monte Carlo method. Our results indicate that the finite sample performance of the three estimators is not robust to the position of the threshold level along the distribution of the threshold variable, especially when a structural change occurs at the tail part of the distribution.

Monte Carlo Comparison for Nonparametric Threshold Estimators

This paper compares the finite sample performance of three non-parametric threshold estimators via Monte Carlo method. Our results show that the finite sample performance of the three estimators is not robust to the relative position of the threshold level along the distribution of threshold variable, especially when a structural change occurs at the tail part of the distribution.