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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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    Cited by:

    1. Badi Baltagi & Chihwa Kao & Sanggon Na, 2011. "Test of hypotheses in panel data models when the regressor and disturbances are possibly non-stationary," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 329-350, December.
    2. A. Mahabbati & A. Izady & M. Mousavi Baygi & K. Davary & S. M. Hasheminia, 2017. "Daily soil temperature modeling using ‘panel-data’ concept," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(8), pages 1385-1401, June.
    3. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    4. Jalan, Jyotsna & Ravallion, Martin, 2001. "Behavioral responses to risk in rural China," Journal of Development Economics, Elsevier, vol. 66(1), pages 23-49, October.
    5. 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.
    6. 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.
    7. You, Jinhong & Zhou, Xian, 2006. "Statistical inference in a panel data semiparametric regression model with serially correlated errors," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 844-873, April.
    8. 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.
    9. H. Baltagi, Badi & Heun Song, Seuck & Cheol Jung, Byoung, 2001. "The unbalanced nested error component regression model," Journal of Econometrics, Elsevier, vol. 101(2), pages 357-381, April.
    10. 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.
    11. Jhun, Myoungshic & Song, Seuck Heun & Jung, Byoung Cheol, 2003. "BLUP in the nested panel regression model with serially correlated errors," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 77-88, October.
    12. Badi H. Baltagi, 1987. "On Estimating from a More General Time-Series Cum Cross-Section Data Structure," The American Economist, Sage Publications, vol. 31(2), pages 69-71, October.
    13. Choi, In, 2002. "Instrumental variables estimation of a nearly nonstationary, heterogeneous error component model," Journal of Econometrics, Elsevier, vol. 109(1), pages 1-32, July.

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    Keywords

    ANOVA spectral decomposition;

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