An Efficient Estimation for Switching Regression Models: A Monte Carlo Study

This paper investigates an efficient estimation method for a class of switching regressions based on the characteristic function (CF). We show that with 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.

• Type: Journal
• Year: 2010
• Where: CSSC
• Authors: Dinghai Xu
• Keywords: Asymptotic Covariance, Asymptotic Covariance Matrix, CSSC 2010, Efficient Estimation Method, Security Privacy