Random effects panel data models with known heteroskedasticity

Julius Schäper () and Rainer Winkelmann

No 445, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: The paper considers two estimators for the linear random effects panel data model with known heteroskedasticity. Examples where heteroskedasticity can be treated as given include panel regression with averaged data, meta regression 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, even in case of misspecification of the scedastic function. Correct confidence interval coverage is obtained 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.

Keywords: Generalized least squares; linear probability model; meta regression (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
Date: 2024-05, Revised 2024-09
New Economics Papers: this item is included in nep-dcm and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.zora.uzh.ch/id/eprint/259907/7/econwp445.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zur:econwp:445

Access Statistics for this paper

More papers in ECON - Working Papers from Department of Economics - University of Zurich Contact information at EDIRC.
Bibliographic data for series maintained by Severin Oswald ().