Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-19T01:51:20.263Z Has data issue: false hasContentIssue false
  • English
  • Français

Fixed Effects Vector Decomposition: A Magical Solution to the Problem of Time-Invariant Variables in Fixed Effects Models?

Published online by Cambridge University Press:  04 January 2017

William Greene
William Greene*
Affiliation:
Department of Economics, Stern School of Business, New York University, New York, NY 10012 e-mail: wgreene@stern.nyu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Plümper and Troeger (2007) propose a three-step procedure for the estimation of a fixed effects (FE) model that, it is claimed, “provides the most reliable estimates under a wide variety of specifications common to real world data.” Their fixed effects vector decomposition (FEVD) estimator is startlingly simple, involving three simple steps, each requiring nothing more than ordinary least squares (OLS). Large gains in efficiency are claimed for cases of time-invariant and slowly time-varying regressors. A subsequent literature has compared the estimator to other estimators of FE models, including the estimator of Hausman and Taylor (1981) also (apparently) with impressive gains in efficiency. The article also claims to provide an efficient estimator for parameters on time-invariant variables (TIVs) in the FE model. None of the claims are correct. The FEVD estimator simply reproduces (identically) the linear FE (dummy variable) estimator then substitutes an inappropriate covariance matrix for the correct one. The consistency result follows from the fact that OLS in the FE model is consistent. The “efficiency” gains are illusory. The claim that the estimator provides an estimator for the coefficients on TIVs in an FE model is also incorrect. That part of the parameter vector remains unidentified. The “estimator” relies upon a strong assumption that turns the FE model into a type of random effects model.

Type
Symposium on Fixed-Effects Vector Decomposition
Information
Political Analysis , Volume 19 , Issue 2 , Spring 2011 , pp. 135 - 146
Copyright
Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology 

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.)

References

Akhter, S., and Daly, K. 2009. Finance poverty: Evidence from fixed effect vector decomposition. Emerging Markets Review 10: 191–206.CrossRefGoogle Scholar
Alemán, J. 2008. Labor market deregulation and industrial conflict in new democracies: A cross-national analysis. Political Studies 56: 830–56.Google Scholar
Brück, T., and Peters, H. 2009. “20 Years of German unification: Evidence on income convergence and heterogeneity,” IZA Discussion Paper 4454.Google Scholar
Buckley, J., and Schneider, M. 2007. Charter schools: Hope or hype? Princeton, NJ: Princeton University Press.Google Scholar
Caporale, G., Rault, C., Sova, R., and Sova, A. 2009. On the bilateral trade effects of free trade agreements between the EU-15 and the CEEC-4 countries. Review of World Economics 145: 189–206.CrossRefGoogle Scholar
Cornwell, C., and Rupert, P. 1988. Efficient estimation with panel data: An empirical comparison of instrumental variable estimators. Journal of Applied Econometrics 3: 149–55.CrossRefGoogle Scholar
Davis, D. 2009. Price and promotion effects of supermarket mergers. Working paper. Department of Economics, South Dakota State University.Google Scholar
Greene, W. 2008. Econometric analysis. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Hansen, M. 2007. Using private contracts to create adoptions from foster care. Working paper No. 2007-03. Department of Economics, American University.Google Scholar
Hausman, J., and Taylor, W. 1981. Panel data and unobservable individual effects. Econometrica 49: 1377–98.CrossRefGoogle Scholar
Mainwaring, S., and Pérez-Liñán, A. 2008. Regime legacies and democratization: Explaining variance in the level of democracy in Latin America, 1978–2004. Political Science Working Paper # 354. Notre Dame, France: Kellogg School.Google Scholar
Plümper, T., and Troeger, V. 2007. Efficient estimation of time-invariant and rarely changing variables in finite sample panel analyses with unit fixed effects. Political Analysis 15: 124–39.Google Scholar
Sova, R., Albu, L., Stancu, I., and Sova, A. 2000. Patterns of foreign direct investment in the New EU countries. Romanian Journal of Economic Forecasting 2: 42–51.Google Scholar
White, H. 1980. A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica 48: 817–38.Google Scholar
Worrall, J. 2008. Racial composition, unemployment, and crime: Dealing with inconsistencies in panel designs. Social Science Research 37: 787–800.CrossRefGoogle ScholarPubMed