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A comparative study of alternative estimators for the unbalanced two-way error component regression model

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  • Badi H. Baltagi
  • Seuck H. Song
  • Byoung C. Jung

Abstract

This paper considers the unbalanced two-way error component model studied by Wansbeek and Kapteyn (1989). Alternative analysis of variance (ANOVA), minimum norm quadratic unbiased and restricted maximum likelihood (REML) estimation procedures are proposed. The mean squared error performance of these estimators are compared using Monte Carlo experiments. Results show that for the estimates of the variance components, the computationally more demanding maximum likelihood (ML) and minimum variance quadratic unbiased (MIVQUE) estimators are recommended, especially if the unbalanced pattern is severe. However, focusing on the regression coefficient estimates, the simple ANOVA methods perform just as well as the computationally demanding ML and MIVQUE methods and are recommended. Copyright Royal Economic Society, 2002

Suggested Citation

  • Badi H. Baltagi & Seuck H. Song & Byoung C. Jung, 2002. "A comparative study of alternative estimators for the unbalanced two-way error component regression model," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 480-493, June.
    • Handle: RePEc:ect:emjrnl:v:5:y:2002:i:2:p:480-493
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    Cited by:

    1. Ford, Timothy C. & Rork, Jonathan C., 2010. "Why buy what you can get for free? The effect of foreign direct investment on state patent rates," Journal of Urban Economics, Elsevier, vol. 68(1), pages 72-81, July.
    2. Dong Kim, 2012. "What is an oil shock? Panel data evidence," Empirical Economics, Springer, vol. 43(1), pages 121-143, August.
    3. Shobande, Olatunji A. & Ogbeifun, Lawrence, 2023. "Pooling cross-sectional and time series data for estimating causality between technological innovation, affluence and carbon dynamics: A comparative evidence from developed and developing countries," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    4. Robert F. Phillips, 2012. "On computing generalized least squares and maximum-likelihood estimates of error-components models with incomplete panels and correlated disturbances," Economics Bulletin, AccessEcon, vol. 32(4), pages 3017-3024.
    5. Fadhuile, A., 2018. "Can we explain pesticide price trend by the regulation changes ?," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277112, International Association of Agricultural Economists.
    6. Badi Baltagi & Seuck Song, 2006. "Unbalanced panel data: A survey," Statistical Papers, Springer, vol. 47(4), pages 493-523, October.
    7. Hou, Jia, 2020. "Independence Status of Territories and the Estimated Trade Effects of Regional Trade Agreements," MPRA Paper 104040, University Library of Munich, Germany.
    8. Sultan, Muyed, 2008. "The Tertiary Sector Is Going to Dominate the World Economy; Should We Worry?," MPRA Paper 14681, University Library of Munich, Germany.
    9. Erik Biørn & Kjersti-Gro Lindquist & Terje Skjerpen, 2002. "Heterogeneity in Returns to Scale: A Random Coefficient Analysis with Unbalanced Panel Data," Journal of Productivity Analysis, Springer, vol. 18(1), pages 39-57, July.
    10. Higney, Anthony & Hanley, Nick & Moro, Mirko, 2022. "The lead-crime hypothesis: A meta-analysis," Regional Science and Urban Economics, Elsevier, vol. 97(C).

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