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Is Adaptive Estimation Useful For Panel Models With Heteroskedasticity In The Individual Specific Error Component? Some Monte Carlo Evidence

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  • Nilanjana Roy

Abstract

This paper first derives an adaptive estimator when heteroskedasticity is present in the individual specific error in an error component model and then compares the finite sample performance of the proposed estimator with various other estimators. While the Monte Carlo results show that the proposed estimator performs adequately in terms of relative efficiency, its performance on the basis of empirical size is quite similar to the other estimators considered.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:emetrv:v:21:y:2002:i:2:p:189-203
    DOI: 10.1081/ETC-120014348
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    References listed on IDEAS

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    6. Rilstone, Paul, 1991. "Some Monte Carlo Evidence on the Relative Efficiency of Parametric and Semiparametric EGLS Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 179-187, April.
    7. Li, Qi & Stengos, Thanasis, 1994. "Adaptive Estimation in the Panel Data Error Component Model with Heteroskedasticity of Unknown Form," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(4), pages 981-1000, November.
    8. 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.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2012. "A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model," Journal of Econometrics, Elsevier, vol. 170(1), pages 164-177.
    4. 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.
    5. Liddle, Brantley & Lung, Sidney, 2010. "Age-Structure, Urbanization, and Climate Change in Developed Countries: Revisiting STIRPAT for Disaggregated Population and Consumption-Related Environmental Impacts," MPRA Paper 59579, University Library of Munich, Germany.
    6. 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.
    7. Choong, Chee-Keong & Baharumshah, Ahmad Zubaidi & Yusop, Zulkornain & Habibullah, Muzafar Shah, 2010. "Private capital flows, stock market and economic growth in developed and developing countries: A comparative analysis," Japan and the World Economy, Elsevier, vol. 22(2), pages 107-117, March.
    8. 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.
    9. Platoni, Silvia & Sckokai, Paolo & Moro, Daniele, 2008. "Panel Data Estimation Techniques for Farm-level Data Model," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44268, European Association of Agricultural Economists.

    More about this item

    Keywords

    Heteroskedasticity; Kernel estimation; Error component model; JEL Classification ; C14; C23;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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