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Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Financial Data

Kenneth A. Froot
The Journal of Financial and Quantitative Analysis
Vol. 24, No. 3 (Sep., 1989), pp. 333-355
DOI: 10.2307/2330815
Stable URL: http://www.jstor.org/stable/2330815
Page Count: 23
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Financial Data
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Abstract

This paper provides a simple method to account for heteroskedasticity and cross-sectional dependence in samples with large cross sections and relatively few time-series observations. The method is motivated by cross-sectional regression studies in finance and accounting. Simulation evidence suggests that these estimators are dependable in small samples and may be useful when generalized least squares is infeasible, unreliable, or computationally too burdensome. We also consider efficiency issues and show that, in principle, asymptotic efficiency can be improved using a technique due to Cragg (1983).

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