Abstract
This paper provides a simple method to account for heteroskesdasticity and cross-sectional dependence in samples with large cross sections and relatively few time series observations. The estimators we derive are motivated by cross-sectional regression studies in finance and accounting. Simulation evidence suggests that the estimators are dependable in small samples and may be useful when generalized least squares is infeasible, unreliable, or computationally too burdensome. The approach allows a relatively small number of time series observations to yield a rich characterization of cross-sectional correlations. 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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