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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Random effects panel data models with known heteroskedasticity
Organization Unit
  • Julius Schäper
  • Rainer Winkelmann
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 445
ISSN 1664-7041
Number of Pages 24
Date 2024
Abstract Text The paper introduces two estimators for the linear random effects panel data model with known heteroskedasticity. Examples where heteroskedasticity can be treated as given include panel regressions with averaged data, meta regressions and the linear probability model. While one estimator builds on the additive random effects assumption, the other, which is simpler to implement in standard software, assumes that the random effect is multiplied by the heteroskedastic standard deviation. Simulation results show that substantial efficiency gains can be realized with either of the two estimators, that they are robust against deviations from the assumed specification, and that the confidence interval coverage equals the nominal level if clustered standard errors are used. Efficiency gains are also evident in an illustrative meta-regression application estimating the effect of study design features on loss aversion coefficients.
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Keywords Generalized least squares, linear probability model, meta regression