Date: 2008
Type: Working Paper
Parameter Estimation in Nonlinear AR-GARCH Models
Working Paper, EUI ECO, 2008/25
MEITZ, Mika, SAIKKONEN, Pentti, Parameter Estimation in Nonlinear AR-GARCH Models, EUI ECO, 2008/25 - https://hdl.handle.net/1814/8770
Retrieved from Cadmus, EUI Research Repository
This paper develops an asymptotic estimation theory for nonlinear autoregressive models
with conditionally heteroskedastic errors. We consider a functional coe cient autoregression
of order p (AR(p)) with the conditional variance specified as a general nonlinear fist 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.
Cadmus permanent link: https://hdl.handle.net/1814/8770
ISSN: 1725-6704
Series/Number: EUI ECO; 2008/25
Publisher: European University Institute