Hostname: page-component-cb9f654ff-hqlzj Total loading time: 0 Render date: 2025-08-22T04:40:08.620Z Has data issue: false hasContentIssue false
  • English
  • Français

Symmetric experimental designs: conditions for equivalence of panel data estimators

Published online by Cambridge University Press:  17 January 2025

Ronald L. Oaxaca  and
David L. Dickinson
Ronald L. Oaxaca*
Affiliation:
Department of Economics, University of Arizona, Tucson, USA IZA, Bonn, Germany LISER, Esch-sur-Alzette, Luxembourg PRESAGE, Texas , USA
David L. Dickinson
Affiliation:
IZA, Bonn, Germany Department of Economics, Appalachian State University, Boone , USA ESI, Orange , USA
*
e-mail: rlo@email.arizona.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper specifies the panel data experimental design condition under which ordinary least squares, fixed effects, and random effects estimators yield identical estimates of treatment effects. This condition is relevant to the large body of laboratory experimental research that generates panel data. Although the point estimates and the true standard errors of the estimated average treatment effects are identical across the three estimators, the estimated standard errors differ. A standard F test as well as asymptotic reasoning guide the choice of which estimated standard errors are the appropriate ones to use for statistical inference.

Keywords

ExperimentsPanel dataEconometricsEstimators

JEL classification

C13: Estimation: General C23: Panel Data Models • Spatio-temporal Models C90: General

Information

Type
Experimental Tools
Information
Journal of the Economic Science Association , Volume 2 , Issue 1 , May 2016 , pp. 85 - 95
Copyright
Copyright © Economic Science Association 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Cason, T. T., Masters, W. A., Sheremeta, R. M. (2010). Entry into winner-take-all and proportional-prize contests: An experimental study. Jounal of Public Economics, 94, 604–611. 10.1016/j.jpubeco.2010.05.006CrossRefGoogle Scholar
De Hoyos, R. E., & Sarafidis, V. Testing for cross-sectional dependence in panel-data models. The Stata Journal, 6(4), 482–496.CrossRefGoogle Scholar
Dickinson, D. L., & Oaxaca, R. L. (2009). Statistical discrimination in labor markets: An experimental analysis. Southern Economic Journal, 76(1), 16–31.CrossRefGoogle Scholar
Dickinson, D. L., & Oaxaca, R. L. (2014). Wages, employment, and statistical discrimination: Evidence from the laboratory. Economic Inquiry, 52(4), 1380–1391.CrossRefGoogle Scholar
Füllbrunn, S., Kreiner, S., & Palan, S. (2015). The value of a fallback option. Central European Journal of Operations Research, 23, 375–388.CrossRefGoogle Scholar
Greene, W. H. (2008). Econometric Analysis, Upper Saddle River: Pearson Prentice Hall.Google Scholar
Judge, G. G. et al. (1980). The Theory and Practice of Econometrics. New York: Wiley.Google Scholar
Khuri, A. I. (1992). Response surface models with random block effects. Technometrics, 34(1), 26–37. 10.2307/1269549CrossRefGoogle Scholar
Mundlak, Y. (1968). On the pooling of time series and cross section data. Econometrica, 46(1), 69–85.CrossRefGoogle Scholar
Oaxaca, R. L., Geisler, I. (2003). Fixed effects models with time invariant variables: A theoretical note. Economic Letters, 80(3), 373–377. 10.1016/S0165-1765(03)00121-6CrossRefGoogle Scholar
Smith, V. L., & Williams, A. W. (1981). On nonbinding price controls in a competitive market. American Economic Review, 71(3), 467—474.Google Scholar