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Some Monte Carlo Evidence on the Relative Efficiency of Parametric and Semiparametric EGLS Estimators

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  • Rilstone, Paul

Abstract

One of the most common practical problems in statistics and econometrics is the estimation of linear regression models with heteroscedastic errors. This article reports the results of a Monte Carlo comparison of various parametric and semiparametric estimated generalized least squares (EGLS) estimators. In small-sized (20) and sometimes medium-sized (50) samples, ordinary least squares dominated the other techniques for low levels of heteroscedasticity. In medium-sized samples, correctly specified EGLS dominated with moderate and large levels of heteroscedasticity. Apart from correctly specified EGLS, a semiparametric approach generally dominated in the medium-sized samples with moderate and large amounts of heteroscedasticity. An additional result is that an incorrectly specified EGLS estimator could, in small samples, yield more precise estimates than the other EGLS techniques. For each of the feasible parametric and semiparametric techniques considered, the usual standard errors and heteroscedasticity-consistent standard errors understated the sample variability of the estimators.

Suggested Citation

  • 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.
    • Handle: RePEc:bes:jnlbes:v:9:y:1991:i:2:p:179-87
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    Cited by:

    1. 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.
    2. Chaudhuri, Saraswata & Renault, Eric, 2023. "Efficient estimation of regression models with user-specified parametric model for heteroskedasticty," The Warwick Economics Research Paper Series (TWERPS) 1473, University of Warwick, Department of Economics.
    3. Balazs Varadi, 2001. "Multiproduct Cost Function Estimation for American Higher Education: Economies of Scale and Scope," CERS-IE WORKING PAPERS 0111, Institute of Economics, Centre for Economic and Regional Studies.
    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. Eric S. Lin & Ta-Sheng Chou, 2018. "Finite-sample refinement of GMM approach to nonlinear models under heteroskedasticity of unknown form," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 1-28, January.

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