Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-09T05:32:10.886Z Has data issue: false hasContentIssue false
  • English
  • Français

On the Fixed-Effects Vector Decomposition

Published online by Cambridge University Press:  04 January 2017

Trevor Breusch ,
Michael B. Ward ,
Hoa Thi Minh Nguyen  and
Tom Kompas
Trevor Breusch*
Affiliation:
Crawford School of Economics and Government, The Australian National University, Canberra, ACT 0200, Australia
Michael B. Ward
Affiliation:
Crawford School of Economics and Government, The Australian National University, Canberra, ACT 0200, Australia e-mail: [email protected]
Hoa Thi Minh Nguyen
Affiliation:
Crawford School of Economics and Government, The Australian National University, Canberra, ACT 0200, Australia e-mail: [email protected]
Tom Kompas
Affiliation:
Crawford School of Economics and Government, The Australian National University, Canberra, ACT 0200, Australia e-mail: [email protected]
*
e-mail: [email protected] (corresponding author)
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper analyzes the properties of the fixed-effects vector decomposition estimator, an emerging and popular technique for estimating time-invariant variables in panel data models with group effects. This estimator was initially motivated on heuristic grounds, and advocated on the strength of favorable Monte Carlo results, but with no formal analysis. We show that the three-stage procedure of this decomposition is equivalent to a standard instrumental variables approach, for a specific set of instruments. The instrumental variables representation facilitates the present formal analysis that finds: (1) The estimator reproduces exactly classical fixed-effects estimates for time-varying variables. (2) The standard errors recommended for this estimator are too small for both time-varying and time-invariant variables. (3) The estimator is inconsistent when the time-invariant variables are endogenous. (4) The reported sampling properties in the original Monte Carlo evidence do not account for presence of a group effect. (5) The decomposition estimator has higher risk than existing shrinkage approaches, unless the endogeneity problem is known to be small or no relevant instruments exist.

Type
Symposium on Fixed-Effects Vector Decomposition
Information
Political Analysis , Volume 19 , Issue 2 , Spring 2011 , pp. 123 - 134
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.)

Footnotes

Authors' note: Supplementary materials for this article are available on the Political Analysis Web site.

References

Baltagi, Badi H., Bresson, Georges, and Pirotte, Alain. 2003. Fixed effects, random effects or Hausman-Taylor? A pretest estimator. Economics Letters 79: 361–9.CrossRefGoogle Scholar
Belke, Ansgar, and Spies, Julia. 2008. Enlarging the EMU to the east: What effects on trade? Empirica 35: 369–89.CrossRefGoogle Scholar
Bound, John, Jaeger, David A., and Baker, Regina M. 1995. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association 90: 443–50.Google Scholar
Breusch, Trevor S., Mizon, Grayham E., and Schmidt, Peter. 1989. Efficient estimation using panel data. Econometrica 57: 695–700.CrossRefGoogle Scholar
Caporale, Guglielmo M., Rault, Christophe, Sova, Robert, and Sova, Anamaria. 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
Davidson, Russell, and MacKinnon, James G. 1993. Estimation and inference in econometrics. New York: Oxford University Press.Google Scholar
Efron, Bradley. 1987. Better bootstrap confidence intervals. Journal of the American Statistical Association 82: 171–85.Google Scholar
Feldstein, Martin. 1974. Errors in variables: A consistent estimator with smaller MSE in finite samples. Journal of the American Statistical Association 69: 990–6.CrossRefGoogle Scholar
Green, Edwin J., and Strawderman, William E. 1991. A James-Stein type estimator for combining unbiased and possibly biased estimators. Journal of the American Statistical Association 86: 1001–6.CrossRefGoogle Scholar
Han, Chirok, and Schmidt, Peter. 2001. The asymptotic distribution of the instrumental variable estimators when the instruments are not correlated with the regressors. Economics Letters 74: 61–6.CrossRefGoogle Scholar
Hausman, Jerry A., and Taylor, William E. 1981. Panel data and unobservable individual effects. Econometrica 49: 1377–98.CrossRefGoogle Scholar
Hoeting, Jennifer A., Madigan, David, Raftery, Adrian E., and Volinsky, Chris T. 1999. Bayesian model averaging: A tutorial. Statistical Science 14: 382–401.Google Scholar
James, W., and Stein, Charles. 1961. Estimation with quadratic loss. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability. Vol. 1, ed. Neyman, J., 361–79. Berkeley, CA; University of California Press.Google Scholar
Judge, George G., and Mittelhammer, Ron C. 2004. A semiparametric basis for combining estimation problems under quadratic loss. Journal of the American Statistical Association 99: 479–87.CrossRefGoogle Scholar
Krogstrup, Signe, and Wälti, Sebastien. 2008. Do fiscal rules cause budgetary outcomes? Public Choice 136: 123–38.CrossRefGoogle Scholar
Mittelhammer, Ron C., and Judge, George G. 2005. Combining estimators to improve structural model estimation and inference under quadratic loss. Journal of Econometrics 128: 1–29.CrossRefGoogle Scholar
Mitze, Timo. 2009. Endogeneity in panel data models with time-varying and time-fixed regressors: to IV or not IV? Ruhr Economic Paper No. 83.Google Scholar
Mundlak, Yair. 1978. On the pooling of time series and cross section data. Econometrica 46: 69–85.Google Scholar
Plümper, Thomas, and Troeger, Vera E. 2007a. 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
Plümper, Thomas, and Troeger, Vera E. 2007b. xtfevd.ado version 2.00 beta. http://www.polsci.org/pluemper/xtfevd.ado.Google Scholar
Sawa, Takamitsu. 1973. The mean square error of a combined estimator and numerical comparison with the TSLS estimator. Journal of Econometrics 1: 115–32.CrossRefGoogle Scholar
Wong, Ka-fu. 1997. Effects on inference of pretesting the exogeneity of a regressor. Economics Letters 56: 267–71.Google Scholar
Wooldridge, Jeffrey M. 2002. Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.Google Scholar
Supplementary material: File

Breusch et al. supplementary material

Data 1

Download Breusch et al. supplementary material(File)
File 71.2 KB
Supplementary material: File

Breusch et al. supplementary material

Data 2

Download Breusch et al. supplementary material(File)
File 861 Bytes
Supplementary material: File

Breusch et al. supplementary material

Table 1

Download Breusch et al. supplementary material(File)
File 5.5 KB
Supplementary material: File

Breusch et al. supplementary material

Table 2

Download Breusch et al. supplementary material(File)
File 11.3 KB