An efficient estimation for switching regression models: A Monte Carlo study

Abstract

This paper investigates an efficient estimation method for a class of switching regressions based on the characteristic function (CF). We show that the exponential weighting function, the CF based estimator can be achieved from minimizing a closed form distance measure. Due to the availability of the analytical structure of the asymptotic covariance, an iterative estimation procedure is developed involving the minimization of a precision measure of the asymptotic covariance matrix. Numerical examples are illustrated via a set of Monte Carlo experiments examining the implentability, finite sample property and efficiency of the proposed estimator.