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Inference in a structural heteroskedastic calibration model

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  • Mário Castro
  • Manuel Galea

Abstract

The main goal of this paper is to study inference in an heteroskedastic calibration model. We embrace a multivariate structural model with known diagonal covariance error matrices, which is a common setup when different measurement methods are compared. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators and a graphical device for model checking are also discussed. Test statistics are proposed for testing hypotheses of interest with the asymptotic chi-square distribution which guarantees correct asymptotic significance levels. Results of simulations comprising point estimation, interval estimation, and hypothesis testing are reported. An application to a real data set is given. Up to best of our knowledge, topics such as model checking and hypotheses testing have received only scarce attention in the literature on calibration models. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Mário Castro & Manuel Galea, 2015. "Inference in a structural heteroskedastic calibration model," Statistical Papers, Springer, vol. 56(2), pages 479-494, May.
    • Handle: RePEc:spr:stpapr:v:56:y:2015:i:2:p:479-494
    DOI: 10.1007/s00362-014-0592-8
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    References listed on IDEAS

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    1. Alexandre Patriota & Artur Lemonte & Heleno Bolfarine, 2011. "Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model," Statistical Papers, Springer, vol. 52(2), pages 455-467, May.
    2. Fahrmeir, L., 1988. "A note on asymptotic testing theory for nonhomogeneous observations," Stochastic Processes and their Applications, Elsevier, vol. 28(2), pages 267-273, June.
    3. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521471626, January.
    4. Filidor Vilca-Labra & Reiko Aoki & Camila Zeller, 2011. "Hypotheses testing for structural calibration model," Statistical Papers, Springer, vol. 52(3), pages 553-565, August.
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