Density estimation for nonlinear parametric models with conditional heteroscedasticity

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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.

Original languageEnglish (US)
Pages (from-to)71-82
Number of pages12
JournalJournal of Econometrics
Issue number1
StatePublished - Mar 1 2010

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


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