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Parameter estimation in nonlinear AR-GARCH models

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Author Info
Mika Meitz
Pentti Saikkonen

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Abstract

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a functional coefficient autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first order generalized autoregressive conditional heteroskedasticity (GARCH (1,1)) model. Strong consistency and asymptotic normality of the global Gaussian quasi maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.

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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 396.

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Date of creation: 2008
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Handle: RePEc:oxf:wpaper:396

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Keywords: AR-GARCH Asymptotic Normality Consistency Nonlinear Time Series Quasi Maximum Likelihood

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models

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