Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters
This paper develops an e±cient method for estimating the discrete mix- tures of normal family based on the continuous empirical characteristic function (CECF). An iterated estimation procedure based on the closed form objective distance function is proposed to improve the estimation effciency. The results from the Monte Carlo simulation reveal that the CECF estimator produces good finite sample properties. In particular, it outperforms the discrete type of methods when the maximum likelihood estimation fails to converge. An empirical example is provided for illustrative purposes.
|Date of creation:||Dec 2008|
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- Knight, John L. & Yu, Jun, 2002.
"Empirical Characteristic Function In Time Series Estimation,"
Cambridge University Press, vol. 18(03), pages 691-721, June.
- Knight, John & Yu, Jun, 1999. "Empirical Characteristic Function in Time Series Estimation," Working Papers 220, Department of Economics, The University of Auckland.
- Jiang, George J & Knight, John L, 2002. "Estimation of Continuous-Time Processes via the Empirical Characteristic Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 198-212, April.
- Kien Tran, 1998. "Estimating mixtures of normal distributions via empirical characteristic function," Econometric Reviews, Taylor & Francis Journals, vol. 17(2), pages 167-183.
- French, Kenneth R., 1980. "Stock returns and the weekend effect," Journal of Financial Economics, Elsevier, vol. 8(1), pages 55-69, March. Full references (including those not matched with items on IDEAS)
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