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Using Bootstrapped Confidence Intervals for Improved Inferences with Seemingly Unrelated Regression Equations

Published online by Cambridge University Press:  11 February 2009

Paul Rilstone  and
Michael Veall
Paul Rilstone
Affiliation:
York University
Michael Veall
Affiliation:
McMaster University
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Abstract

The usual standard errors for the regression coefficients in a seemingly unrelated regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper, Monte Carlo evidence is reported which indicates that bootstrapping can result in substantially better inferences when applied to t-ratios rather than to standard errors.

Information

Type
Research Article
Information
Econometric Theory , Volume 12 , Issue 3 , August 1996 , pp. 569 - 580
Copyright
Copyright © Cambridge University Press 1996

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References

REFERENCES

Atkinson, S.E. & Wilson, P.W. (1992) The bias of bootstrapped versus conventional standard errors in the general linear and SUR models. Econometric Theory 8, 258–275.CrossRefGoogle Scholar
Beran, R.J. (1988) Prepivoting test statistics: A bootstrap view of asymptotic refinements. Journal of the American Statistical Association 83, 687–697.CrossRefGoogle Scholar
Efron, B. (1979) Bootstrap methods: Another look at the jackknife. Annals of Mathematical Statistics 7, 1–26.Google Scholar
Efron, B. (1987) Better bootstrap confidence intervals (with discussion). Journal of the American Statistical Association 82, 171–200.CrossRefGoogle Scholar
Efron, B. & Tibshirani, R. (1993) An Introduction to the Bootstrap. New York: Chapman and Hall.CrossRefGoogle Scholar
Hall, P. (1988) Theoretical comparison of bootstrap confidence intervals (with discussion). Annals of Statistics 16, 927–953.Google Scholar
Hall, P. (1992) The Bootstrap and Edgeworth Expansion. New York: Springer-Verlag.CrossRefGoogle Scholar
Hartigan, J.A. (1986) Comment. Statistical Science 1, 75–77.CrossRefGoogle Scholar
Marais, M.L. (1986) On the Finite Sample Performance of Estimated Generalized Least Squares in Seemingly Unrelated Regression: Nonnormal Disturbances and Alternative Standard Error Estimators. Working paper, Graduate School of Business, University of Chicago.Google Scholar
Rilstone, P. (1993) Some improvements for bootstrapping regression estimators under first-order serial correlation. Economics Letters 42, 335–339.CrossRefGoogle Scholar
Singh, K. (1981) On the asymptotic accuracy of Efron's bootstrap. Annals of Statistics 9, 1187–1195.CrossRefGoogle Scholar
Wu, C.F.J. (1986) Jackknife, bootstrap and other resampling methods in regression analysis. Annals of Statistics 14, 1261–1295.Google Scholar