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Bootstrap methods for heteroskedastic regression models: evidence on estimation and testing

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  • F. Cribari-Neto
  • S. G. Zarkos

Abstract

This paper uses Monte Carlo simulation analysis to study the finite-sample behavior of bootstrap estimators and tests in the linear heteroskedastic model. We consider four different bootstrapping schemes, three of them specifically tailored to handle heteroskedasticity. Our results show that weighted bootstrap methods can be successfully used to estimate the variances of the least squares estimators of the linear parameters both under normality and under nonnormality. Simulation results are also given comparing the size and power of the bootstrapped Breusch-Pagan test with that of the original test and of Bartlett and Edgeworth-corrected tests. The bootstrap test was found to be robust against unfavorable regression designs.

Suggested Citation

  • F. Cribari-Neto & S. G. Zarkos, 1999. "Bootstrap methods for heteroskedastic regression models: evidence on estimation and testing," Econometric Reviews, Taylor & Francis Journals, vol. 18(2), pages 211-228.
    • Handle: RePEc:taf:emetrv:v:18:y:1999:i:2:p:211-228
    DOI: 10.1080/07474939908800440
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    Keywords

    Bartlett-type correction; bootstrap; Edgeworth expansion; heteroskedasticity; Lagrange multiplier test; score test; weighted bootstrap; JEL CLASSIFICATION:C12; C13; C15;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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