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A note on spectral decomposition and maximum likelihood estimation in models with balanced data

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  • Wansbeek, Tom
  • Kapteyn, Arie

Abstract

A simple derivation of the spectral decomposition of the covariance matrix for a general multi-way variance components model is presented. So-called balanced data are assumed to be available. Spectral decomposition is exploited to derive the information matrix and the first-order conditions for the maximum likelihood estimation of the variance components parameters.

Suggested Citation

  • Wansbeek, Tom & Kapteyn, Arie, 1983. "A note on spectral decomposition and maximum likelihood estimation in models with balanced data," Statistics & Probability Letters, Elsevier, vol. 1(4), pages 213-215, June.
  • Handle: RePEc:eee:stapro:v:1:y:1983:i:4:p:213-215
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    2. Jalan, Jyotsna & Ravallion, Martin, 2001. "Behavioral responses to risk in rural China," Journal of Development Economics, Elsevier, vol. 66(1), pages 23-49, October.
    3. Baltagi, Badi H. & Liu, Long, 2013. "Estimation and prediction in the random effects model with AR(p) remainder disturbances," International Journal of Forecasting, Elsevier, vol. 29(1), pages 100-107.
    4. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    5. Théophile AZOMAHOU & Phu NGUYEN VAN & Marcus WAGNER, 2001. "Determinants of Environmental and Economic Performance of Firms: An Empirical Analysis of the European Paper Industry," Working Papers of BETA 2001-22, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    6. Fischer, Manfred M. & Scherngell, Thomas & Reismann, Martin, 2008. "Knowledge spillovers and total factor productivity. Evidence using a spatial panel data model," MPRA Paper 77762, University Library of Munich, Germany.

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    Keywords

    ANOVA spectral decomposition;

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