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

Froot, Kenneth A.·1989·The Journal of Financial and Quantitative Analysis

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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Metadata

Authors
Froot, K. A.
Venue
The Journal of Financial and Quantitative Analysis
Publisher
JSTOR
Published
1989-09
Volume
24
Issue
3
Pages
333
ISSN
0022-1090 (ISSN-L)
OpenAlex
W2029942536
Cited by
621
References
0
FWCI
9.22
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Financial Data. https://app.cochat.ai/papers/10.2307/2330815
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Froot, K. A. (1989). Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Financial Data. The Journal of Financial and Quantitative Analysis. https://doi.org/10.2307/2330815

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.