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Random effects panel data models with known heteroskedasticity


Schäper, Julius; Winkelmann, Rainer (2024). Random effects panel data models with known heteroskedasticity. Working paper series / Department of Economics 445, University of Zurich.

Abstract

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.

Abstract

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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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Economics
Working Paper Series > Department of Economics
Dewey Decimal Classification:330 Economics
JEL Classification:C23
Uncontrolled Keywords:Generalized least squares, linear probability model, meta regression
Scope:Discipline-based scholarship (basic research)
Language:English
Date:May 2024
Deposited On:30 May 2024 07:52
Last Modified:30 May 2024 07:52
Series Name:Working paper series / Department of Economics
Number of Pages:24
ISSN:1664-7041
OA Status:Green
Related URLs:https://www.econ.uzh.ch/en/research/workingpapers.html
  • Content: Published Version
  • Language: English