An Efficient Estimation for Switching Regression Models: A Monte Carlo Study
This paper investigates an e±cient 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 e±ciency of the proposed estimator.
|Date of creation:||Apr 2009|
|Date of revision:||Apr 2009|
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- Dinghai Xu & John Knight, 2011.
"Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters,"
Taylor & Francis Journals, vol. 30(1), pages 25-50.
- Dinghai Xu & John Knight, 2008. "Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters," Working Papers 08006, University of Waterloo, Department of Economics.
- Jun Yu, 2004. "Empirical Characteristic Function Estimation and Its Applications," Econometric Reviews, Taylor & Francis Journals, vol. 23(2), pages 93-123.
- Kien Tran, 1998. "Estimating mixtures of normal distributions via empirical characteristic function," Econometric Reviews, Taylor & Francis Journals, vol. 17(2), pages 167-183. Full references (including those not matched with items on IDEAS)
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