Doubly penalized likelihood estimator in heteroscedastic regression
A penalized likelihood estimation procedure is developed for heteroscedastic regression. A distinguishing feature of the new methodology is that it estimates both the mean and variance functions simultaneously without parametric assumption for either. An efficient implementation of the estimating procedure is also provided. The procedure is illustrated by a Monte Carlo example. A potential generalization, and application to the covariance modeling problem in numerical weather prediction is noted.
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Volume (Year): 69 (2004)
Issue (Month): 1 (August)
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References listed on IDEAS
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- Jianqing Fan & Qiwei Yao, 1998. "Efficient estimation of conditional variance functions in stochastic regression," LSE Research Online Documents on Economics 6635, London School of Economics and Political Science, LSE Library.
- Gallant, A. Ronald & Tauchen, George, 1997.
"Estimation Of Continuous-Time Models For Stock Returns And Interest Rates,"
Cambridge University Press, vol. 1(01), pages 135-168, January.
- Tauchen, George E. & Gallant, A. Ronald, 1995. "Estimation of Continuous Time Models for Stock Returns and Interest Rates," Working Papers 95-53, Duke University, Department of Economics.
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