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Curto, J., Pinto, J., Morais, A. & Lourenço, I. (2011). The heteroskedasticity-consistent covariance estimator in accounting. Review of Quantitative Finance and Accounting. 37 (4), 427-449

J. J. Curto et al.,  "The heteroskedasticity-consistent covariance estimator in accounting", in Review of Quantitative Finance and Accounting, vol. 37, no. 4, pp. 427-449, 2011

@article{curto2011_1732196522099,
	author = "Curto, J. and Pinto, J. and Morais, A. and Lourenço, I.",
	title = "The heteroskedasticity-consistent covariance estimator in accounting",
	journal = "Review of Quantitative Finance and Accounting",
	year = "2011",
	volume = "37",
	number = "4",
	doi = "10.1007/s11156-010-0212-1",
	pages = "427-449",
	url = "http://link.springer.com/article/10.1007%2Fs11156-010-0212-1"
}

TY  - JOUR
TI  - The heteroskedasticity-consistent covariance estimator in accounting
T2  - Review of Quantitative Finance and Accounting
VL  - 37
IS  - 4
AU  - Curto, J.
AU  - Pinto, J.
AU  - Morais, A.
AU  - Lourenço, I.
PY  - 2011
SP  - 427-449
SN  - 0924-865X
DO  - 10.1007/s11156-010-0212-1
UR  - http://link.springer.com/article/10.1007%2Fs11156-010-0212-1
AB  - The main purpose of this paper is to compare the White (1980) heteroskedasticity-consistent (HC) covariance matrix estimator with alternative estimators. Many regression packages compute the White (1980) heteroskedasticity-consistent (HC) covariance matrix estimator. The common procedure in Accounting and Finance research to deal with the heteroskedasticity problem is based on this estimator, despite its worse finite-samples properties when compared with other consistent estimators. In this paper we compare several HC covariance matrix estimators based on a sample of 3706 European listed companies from Austria, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden and the United Kingdom. We conclude that HC standard errors increase when finite-samples more appropriate estimators are considered and in the most part of countries the Ohlson (1995) model coefficients estimates became statistically insignificant. This can be explained by the high leverage points in the design matrix. To the best of our knowledge it is the first time that these alternative estimators are compared with the one of White (1980) in accounting research
ER  -