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

Risto D. H. Heijmans and Jan Magnus ()

Econometric Theory, 1986, vol. 2, issue 3, 374-412

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.

Date: 1986
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:2:y:1986:i:03:p:374-412_01

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