PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS

Mika Meitz () and Pentti Saikkonen

Econometric Theory, 2011, vol. 27, issue 6, 1236-1278

Abstract: This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear 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. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. 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.

Date: 2011
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Related works:
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)
Working Paper: Parameter estimation in nonlinear AR-GARCH models (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:27:y:2011:i:06:p:1236-1278_00

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