Density estimation for nonlinear parametric models with conditional heteroscedasticity

Zhibiao Zhao ()

Journal of Econometrics, 2010, vol. 155, issue 1, 71-82

Abstract: This 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.

Keywords: Bahadur; representation; Conditional; heteroscedasticity; Density; estimation; Fisher; information; Nonlinear; time; series; Nonparametric; kernel; density; Stationary; density; Stochastic; regression (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:155:y:2010:i:1:p:71-82

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