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Adaptive estimation of heteroskedastic error component model

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  • BALTAGI B.
  • BRESSON G.
  • PIROTTE A.

Abstract

This paper checks the sensitivity of two adaptive heteroskedastic estimators suggested by Li and Stengos (1994) and Roy (2002) for an error component regression model to misspecification of the form of heteroskedasticity. In particular, we run Monte Carlo experiments using the heteroskedasticity setup by Li and Stengos (1994) to see how the misspecified Roy (2002) estimator performs. Next, we use the heteroskedasticity setup by Roy (2002) to see how the misspecified Li and Stengos (1994) estimator performs. We also check the sensitivity of these results to the choice of the smoothing parameters, the sample size, and the degree of heteroskedasticity. We find that the Li and Stengos (1994) estimator performs better under this type of misspecification than the corresponding estimator of Roy (2002). However, the former estimator is sensitive to the choice of the bandwidth.
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Suggested Citation

  • Baltagi B. & Bresson G. & Pirotte A., 2004. "Adaptive estimation of heteroskedastic error component model," Working Papers ERMES 0402, ERMES, University Paris 2.
  • Handle: RePEc:erm:papers:0402
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    Cited by:

    1. Juhl, Ted & Sosa-Escudero, Walter, 2014. "Testing for heteroskedasticity in fixed effects models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 484-494.
    2. Bresson G. & Hsiao C. & Pirotte A., 2007. "Assessing the Contribution of R&D to Total Factor Productivity – a Bayesian Approach to Account for Heterogeneity And Heteroscedasticity," Working Papers ERMES 0708, ERMES, University Paris 2.
    3. 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.
    4. Anderson, John & Sutherland, Dylan, 2015. "Developed economy investment promotion agencies and emerging market foreign direct investment: The case of Chinese FDI in Canada," Journal of World Business, Elsevier, vol. 50(4), pages 815-825.
    5. 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.

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