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On the first-order efficiency and asymptotic normality of the maximum likelihood estimator obtained from dependent observations

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  • Heijmans, R
  • Magnus, J

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 third-order derivatives to exist; moreover, the conditions will appear to be ready verifiable. This paper builds on Witting and None's result concerning the asymptotic normality of the maximum likelihood estimator obtained from dependent and identically distributed observations, and on a martingale theorem by McLeish.

Suggested Citation

  • Heijmans, R & Magnus, J, 1984. "On the first-order efficiency and asymptotic normality of the maximum likelihood estimator obtained from dependent observations," University of Amsterdam, Actuarial Science and Econometrics Archive 293077, University of Amsterdam, Faculty of Economics and Business.
    • Handle: RePEc:ags:amstas:293077
    DOI: 10.22004/ag.econ.293077
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    Cited by:

    1. Cem Ertur & Thiaw Kalidou, 2005. "Growth and Spatial Dependence - The Mankiw, Romer and Weil model revisited," ERSA conference papers ersa05p660, European Regional Science Association.
    2. Jan R. Magnus & Andrey L. Vasnev, 2007. "Local sensitivity and diagnostic tests," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 166-192, March.
    3. 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.
    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.
    5. 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.
    6. Yoonsuk Lee & B. Wade Brorsen, 2017. "Permanent shocks and forecasting with moving averages," Applied Economics, Taylor & Francis Journals, vol. 49(12), pages 1213-1225, March.

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    Keywords

    Research Methods/ Statistical Methods;

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