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Adaptive Estimation in ARCH Models

Published online by Cambridge University Press:  11 February 2009

Oliver Linton
Oliver Linton
Affiliation:
Nuffield College
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Abstract

We construct efficient estimators of the identifiable parameters in a regression model when the errors follow a stationary parametric ARCH(P) process. We do not assume a functional form for the conditional density of the errors, but do require that it be symmetric about zero. The estimators of the mean parameters are adaptive in the sense of Bickel [2]. The ARCH parameters are not jointly identifiable with the error density. We consider a reparameterization of the variance process and show that the identifiable parameters of this process are adaptively estimable.

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Articles
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Econometric Theory , Volume 9 , Issue 4 , August 1993 , pp. 539 - 569
Copyright
Copyright © Cambridge University Press 1993

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