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

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.

[1]  Gary R. Saxonhouse,et al.  Estimated Parameters as Dependent Variables , 1976 .

[2]  M. Hansen.  Using Private Contracts to Create Adoptions from Foster Care , 2007 .

[3]  Lucian-Liviu Albu,et al.  Patterns of Foreign Direct Investment in the New EU Countries , 2009 .

[4]  Jerry A. Hausman,et al.  Panel Data and Unobservable Individual Effects , 1981 .

[5]  Peter Rupert,et al.  Efficient estimation with panel data: An empirical comparison of instrumental variables estimators , 1988 .

[6]  J. A. Eausman.  PANEL DATA AND UNOBSERVABLE , 2011 .

[7]  Helen Conley.  REGIME LEGACIES AND DEMOCRATIZATION: EXPLAINING VARIANCE IN THE LEVEL OF DEMOCRACY IN LATIN AMERICA, 1978—2004 , 2008 .

[8]  Thomas Plümper,et al.  Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects , 2007, Political Analysis.

[9]  Christophe Rault,et al.  On the bilateral trade effects of free trade agreements between the EU-15 and the CEEC-4 countries , 2008, SSRN Electronic Journal.

[10]  John L Worrall,et al.  Racial composition, unemployment, and crime: dealing with inconsistencies in panel designs. , 2008, Social science research.

[11]  H. White.  A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity , 1980 .

[12]  Tilman Brück,et al.  20 Years of German Unification: Evidence on Income Convergence and Heterogeneity , 2009, SSRN Electronic Journal.

[13]  J. Alemán.  Labor Market Deregulation and Industrial Conflict in New Democracies: A Cross-National Analysis , 2008 .

[14]  A. Pérez-Liñán,et al.  REGIME LEGACIES AND DEMOCRATIZATION : EXPLAINING VARIANCE IN THE LEVEL OF DEMOCRACY IN LATIN AMERICA , 1978 — 2004 , 2008 .

[15]  K. Daly,et al.  Finance and poverty : evidence from fixed effect vector decomposition , 2009 .

[16]  Mark Schneider,et al.  Charter Schools: Hope or Hype? , 2007 .

[17]  David E. Davis.  Price and Promotion Effects of Supermarket Mergers , 2010 .

[18]  W. Greene,et al.  计量经济分析 = Econometric analysis , 2009 .