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

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  • Dinghai Xu

    (Department of Economics, University of Waterloo)

Abstract

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.

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File URL: http://economics.uwaterloo.ca/documents/EfficientPaperXu.pdf
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Bibliographic Info

Paper provided by University of Waterloo, Department of Economics in its series Working Papers with number 0903.

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Length: 22 pages
Date of creation: Apr 2009
Date of revision: Apr 2009
Handle: RePEc:wat:wpaper:0903

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Keywords: Switching Regression model; Characteristic Function; Integrated Squared Error; Gaussian Mixtures.;

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  1. Dinghai Xu & John Knight, 2011. "Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 25-50.
  2. Kien Tran, 1998. "Estimating mixtures of normal distributions via empirical characteristic function," Econometric Reviews, Taylor & Francis Journals, vol. 17(2), pages 167-183.
  3. Jun Yu, 2004. "Empirical Characteristic Function Estimation and Its Applications," Econometric Reviews, Taylor & Francis Journals, vol. 23(2), pages 93-123.
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