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

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  • Heijmans, R.D.H.
  • Magnus, J.R.

    (Tilburg University, School of Economics and Management)

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.
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Suggested Citation

  • Heijmans, R.D.H. & Magnus, J.R., 1986. "On the first-order efficiency and asymptotic normality of maximum likelihood estimators obtained from dependent observations," Other publications TiSEM b2fc9176-e950-4580-90e6-5, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:b2fc9176-e950-4580-90e6-5748ce9aa0a9
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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. 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.
    3. 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.
    4. Jan R. Magnus & Andrey L. Vasnev, 2007. "Local sensitivity and diagnostic tests," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 166-192, March.
    5. 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.
    6. 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.

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