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On The First-Order Efficiency And Asympotic Normality Of The Maximum Likelihood Estimator Obtained From Dependent Observations

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  • Heijmans, Risto
  • Magnus, Jan

Abstract

In this paper we study the first-order efficiency and asymptotic normality of the maximum likelihood estimator obtained from dependent observations. Our conditions are somewhat weaker than usual, in that we do not require convergences in probability to be uniform or thirdorder derivatives to exist; moreover, the conditions will appear to be readily verifiable. This paper builds on Witting and Nalle's result concerning the asymptotic normality of the maximum likelihood estimator obtained from independent and identically distributed observations, and on a martingale theorem by McLeish.

Suggested Citation

  • Heijmans, Risto & Magnus, Jan, 1983. "On The First-Order Efficiency And Asympotic Normality Of The Maximum Likelihood Estimator Obtained From Dependent Observations," University of Amsterdam, Actuarial Science and Econometrics Archive 293068, University of Amsterdam, Faculty of Economics and Business.
    • Handle: RePEc:ags:amstas:293068
    DOI: 10.22004/ag.econ.293068
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    Cited by:

    1. Magnus, J.R. & Vasnev, A.L., 2004. "Local Sensitivity and Diagnostic Tests," Other publications TiSEM 10722abe-f848-4bfa-a82d-6, Tilburg University, School of Economics and Management.
    2. Kevin W. Lu, 2022. "Calibration for multivariate Lévy-driven Ornstein-Uhlenbeck processes with applications to weak subordination," Statistical Inference for Stochastic Processes, Springer, vol. 25(2), pages 365-396, July.
    3. Giet, Ludovic & Lubrano, Michel, 2008. "A minimum Hellinger distance estimator for stochastic differential equations: An application to statistical inference for continuous time interest rate models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2945-2965, February.
    4. Abadir, Karim M. & Distaso, Walter, 2007. "Testing joint hypotheses when one of the alternatives is one-sided," Journal of Econometrics, Elsevier, vol. 140(2), pages 695-718, October.

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    Research Methods/ Statistical Methods;

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