Density estimation for nonlinear parametric models with conditional heteroscedasticity
AbstractThis article studies density and parameter estimation problems for nonlinear parametric models with conditional heteroscedasticity. We propose a simple density estimate that is particularly useful for studying the stationary density of nonlinear time series models. Under a general dependence structure, we establish the root n consistency of the proposed density estimate. For parameter estimation, a Bahadur type representation is obtained for the conditional maximum likelihood estimate. The parameter estimate is shown to be asymptotically efficient in the sense that its limiting variance attains the Cramér-Rao lower bound. The performance of our density estimate is studied by simulations.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 155 (2010)
Issue (Month): 1 (March)
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Web page: http://www.elsevier.com/locate/jeconom
Bahadur representation Conditional heteroscedasticity Density estimation Fisher information Nonlinear time series Nonparametric kernel density Stationary density Stochastic regression;
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