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

  • Risto D. H. Heijmans (a1) and Jan R. Magnus (a2)
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.

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COPYRIGHT: © Cambridge University Press 1986
References
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Econometric Theory
  • ISSN: 0266-4666
  • EISSN: 1469-4360
  • URL: /core/journals/econometric-theory
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