 Inference in a structural heteroskedastic calibration modelMário Castro () and Manuel Galea () Statistical Papers, 2015, vol. 56, issue 2, 479-494 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 Keywords: EM algorithm; Calibration; Estimation; Hypotheses testing; Maximum likelihood; Measurement error models; Structural models; 62J05; 62J99 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:56:y:2015:i:2:p:479-494 Ordering information: This journal article can be ordered from DOI: 10.1007/s00362-014-0592-8 Access Statistics for this article Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller More articles in Statistical Papers from Springer |
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