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).
Keywords
Covariance matrixHeteroscedasticityEstimationEconometricsEstimation of covariance matricesCovarianceMatrix (chemical analysis)EconomicsMathematicsStatisticsChemistry
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Publication Info
- Year
- 1989
- Type
- article
- Volume
- 24
- Issue
- 3
- Pages
- 333-333
- Citations
- 635
- Access
- Closed
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Cite This
Kenneth Froot
(1989).
Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Financial Data.
Journal of Financial and Quantitative Analysis
, 24
(3)
, 333-333.
https://doi.org/10.2307/2330815
Identifiers
- DOI
- 10.2307/2330815