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Asymptotic Normmality of Maximum Likelihood Estimators Obtained from Normally Distributed but Dependent Observations

Published online by Cambridge University Press:  18 October 2010

Risto D. H. Heijmans  and
Jan R. Magnus
Risto D. H. Heijmans
Affiliation:
University of Amsterdam
Jan R. Magnus
Affiliation:
London School of Economics
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Abstract

In this article we aim to establish intuitively appealing and verifiable conditions for the first-order efficiency and asymptotic normality of ML estimators in a multi-parameter framework, assuming joint normality but neither the independence nor the identical distribution of the observations. We present five theorems (and a large number of lemmas and propositions), each being a special case of its predecessor.

Type
Articles
Information
Econometric Theory , Volume 2 , Issue 3 , December 1986 , pp. 374 - 412
Copyright
Copyright © Cambridge University Press 1986

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References

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