An investigation of an unbiased correction for heteroskedasticity and the effects of misspecifying the skedastic function
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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- David A. Belsley, "undated".
"A Small-Sample Correction for Testing for gth-Order Serial Correlation with Artificial Regressions,"
Computing in Economics and Finance 1996
_008, Society for Computational Economics.
- Belsley, David A, 1997. "A Small-Sample Correction for Testing for gth-Order Serial Correlation with Artificial Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 10(3), pages 197-229, August.
- 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.
- Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
- MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-146, March.
- Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
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