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A Generalized Error Component Model with Heteroscedastic Disturbances

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  • Baltagi, Badi H
  • Griffin, James M

Abstract

This paper generalizes the one-way error component model from the homoskedastic to the heteroskedastic case. Unlike P. Mazodier and A. Trognon's (1978) heteroskedastic two-way error component model, an explicit form for V11/2 is obtained that allows the computation of generalized least squares as a simple modification of J. A. Hausman's (1978) procedure for the homoskedastic case. Two methods for estimating the variance components are proposed. The first estimates the variance components based on ordinary least squares and within residuals. The second uses the minimum norm quadratic unbiased estimation procedure suggested by C. R. Rao (1970). These methods are then applied to the estimation of gasoline demand for eighteen OECD countries over the period 1964-78. Copyright 1988 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Suggested Citation

  • Baltagi, Badi H & Griffin, James M, 1988. "A Generalized Error Component Model with Heteroscedastic Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(4), pages 745-753, November.
  • Handle: RePEc:ier:iecrev:v:29:y:1988:i:4:p:745-53
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    Cited by:

    1. Chen, Kevin Z. & D. Meilke, Karl & Turvey, Calum, 1999. "Income risk and farm consumption behavior," Agricultural Economics, Blackwell, vol. 20(2), pages 173-183, March.
    2. Baltagi, Badi H. & Jung, Byoung Cheol & Song, Seuck Heun, 2010. "Testing for heteroskedasticity and serial correlation in a random effects panel data model," Journal of Econometrics, Elsevier, vol. 154(2), pages 122-124, February.
    3. repec:ebl:ecbull:v:3:y:2003:i:15:p:1-4 is not listed on IDEAS
    4. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2006. "Joint LM test for homoskedasticity in a one-way error component model," Journal of Econometrics, Elsevier, vol. 134(2), pages 401-417, October.
    5. Heshmati, Almas, 1994. "Estimating random effects production function models with selectivity bias: an application to Swedish crop producers," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 11(2-3), December.
    6. Eduardo Fé, 2012. "Instrumental variable estimation of heteroskedasticity adaptive error component models," Statistical Papers, Springer, vol. 53(3), pages 577-615, August.
    7. Almas Heshmati & Subal C. Kumbhakar, 2010. "Technical Change and Total Factor Productivity Growth: The Case of Chinese Provinces," TEMEP Discussion Papers 201054, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Feb 2010.
    8. Georges Bresson & Cheng Hsiao & Alain Pirotte, 2011. "Assessing the contribution of R&D to total factor productivity—a Bayesian approach to account for heterogeneity and heteroskedasticity," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 435-452, December.
    9. Subal C. Kumbhakar, 1996. "Le ralentissement de la productivité des entreprises d'électricité au Texas : le rôle des marges, des rendements d'échelle et du progrès technique," Économie et Prévision, Programme National Persée, vol. 126(5), pages 77-89.
    10. repec:ebl:ecbull:v:3:y:2008:i:75:p:1-10 is not listed on IDEAS
    11. Kouassi, Eugene & Mougoué, Mbodja & Sango, Joel & Bosson Brou, J.M. & Amba, Claude M.O. & Salisu, Afeez Adebare, 2014. "Testing for heteroskedasticity and spatial correlation in a two way random effects model," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 153-171.
    12. Shantanu Dutta & Om Narasimhan & Surendra Rajiv, 1999. "Success in High-Technology Markets: Is Marketing Capability Critical?," Marketing Science, INFORMS, pages 547-568.
    13. Nilanjana Roy, 2002. "Is Adaptive Estimation Useful For Panel Models With Heteroskedasticity In The Individual Specific Error Component? Some Monte Carlo Evidence," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 189-203.
    14. Eduardo Fé Rodríguez, 2009. "Adaptive Instrumental Variable Estimation of Heteroskedastic Error Component Models," The School of Economics Discussion Paper Series 0921, Economics, The University of Manchester.
    15. Majumdar, Raju, 2012. "The role of secured debt in resolving agency conflicts and problems of asymmetric information: Indian evidence," MPRA Paper 37925, University Library of Munich, Germany.
    16. Nilanjana Roy, 1999. "Is Adaptive Estimation Useful for Panel Models With Heteroskedasticity in the Unit-Specific Error Component? Some Monte Carlo Evidence," Econometrics Working Papers 9913, Department of Economics, University of Victoria.
    17. Shiu, Alice & Heshmati, Almas, 2006. "Technical Change and Total Factor Productivity Growth for Chinese Provinces: A Panel Data Analysis," Ratio Working Papers 98, The Ratio Institute.
    18. M. Giulietti & S. Mccorriston & P. Osborne, 2004. "Foreign direct investment in the UK: evidence from a disaggregated panel of the UK food sector," Applied Economics, Taylor & Francis Journals, vol. 36(7), pages 653-663.
    19. Baltagi, Badi H. & Song, Seuck Heun & Kwon, Jae Hyeok, 2009. "Testing for heteroskedasticity and spatial correlation in a random effects panel data model," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2897-2922, June.

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