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An Investigation Of An Unbiased Corection For Heteroskedasticity And The Effects Of Misspecifying The Skedastic Function

  • David A. Belsley

    (Boston College)

The traditional two-step procedure for correcting for heteroskedasticity uses a consistent but biased estimator for the variances $\bfg\sigma_t^2$ in enacting the second step. An estimator is developed here that is unbiased in the presence of heteroskedasticity. Its behavior is examined along with the traditional estimator and another known to be unbiased in the absence of heteroskedasticity. The behavior of these corrective methods is also examined when the form and arguments of the skedastic function are misspecified. This is accomplished using Monte Carlo studies of several situations of interest.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 154.

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Date of creation: 05 Jul 2000
Date of revision:
Handle: RePEc:sce:scecf0:154
Contact details of provider: Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain
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  1. David A. Belsley, 1996. "A Small-Sample Correction for Testing for gth-Order Serial Correlation with Artificial Regressions," Boston College Working Papers in Economics 331., Boston College Department of Economics.
  2. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-46, March.
  3. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-94, September.
  4. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
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