Parameter estimation in nonlinear AR-GARCH models

Mika Meitz () and Pentti Saikkonen

No 396, Economics Series Working Papers from University of Oxford, Department of Economics

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.

Keywords: AR-GARCH; Asymptotic normality; Consistency; Nonlinear time series; Quasi maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Date: 2008-06-01
New Economics Papers: this item is included in nep-ets and nep-ore
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS (2011)
Working Paper: Parameter estimation in nonlinear AR–GARCH models (2010)
Working Paper: Parameter estimation in nonlinear AR-GARCH models (2008)
Working Paper: Parameter Estimation in Nonlinear AR-GARCH Models (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:oxf:wpaper:396

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