A note on the covariate-dependent kink threshold regression model for panel data

Abstract

This article provides new estimating and testing procedures for the panel covariate-dependent kink threshold regression model. By utilizing a linearization technique, we develop an efficient estimator that offers a computationally viable alternative to the grid-search approach. Then we propose a novel testing procedure based on the score statistic to examine the existence of the covariate-dependent kink effect. The test statistic only requires fitting the model in the absence of varying kink threshold, greatly reducing computing time. Extensive simulation studies demonstrate that the proposed method outperforms existing methodologies. To further illustrate their performance, we utilize a real-data analysis. The results demonstrate the practicality and effectiveness of our approach in a wide range of scenarios.