Further improvements in the calculation of Censored Quantile Regressions

Censored Quantile Regressions of Powell (1984, 1986) are very powerful inferencing tools in economics and engineering. As the calculation of censored quantile regressions involves minimizing a nonconvex and nondifferentiable function, global optimization techniques can be the only breakthroughs. The first implementation of a global optimization technique, namely Threshold Accepting of Fitzenberger and Winker (1998, 2007), is challenged by the Genetic Algorithm (GA) in this paper. The results show that the GA provides substantial improvements over Threshold Accepting for cases with randomly distributed censoring points.