Breusch, Trevor and Ward, Michael B. and Nguyen, Hoa and Kompas, Tom (2010): On the fixedeffects vector decomposition. Forthcoming in: Political Analysis
This is the latest version of this item.

PDF
MPRA_paper_26767.pdf Download (187kB)  Preview 
Abstract
This paper analyses the properties of the fixedeffects vector decomposition estimator, an emerging and popular technique for estimating timeinvariant variables in panel data models with unit 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 threestage 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 which finds: (1) The estimator reproduces exactly classical fixedeffects estimates for timevarying variables. (2) The standard errors recommended for this estimator are too small for both timevarying and timeinvariant variables. (3) The estimator is inconsistent when the timeinvariant variables are endogenous. (4) The reported sampling properties in the original Monte Carlo evidence are incorrect. (5) We recommend an alternative shrinkage estimator that has superior risk properties to the decomposition estimator, unless the endogeneity problem is known to be small or no relevant instruments exist.
Item Type:  MPRA Paper 

Original Title:  On the fixedeffects vector decomposition 
Language:  English 
Keywords:  panel data models; fixedeffects vector decomposition; instrumental variables; inconsistent estimator; incorrect standard errors; improved shrinkage estimator 
Subjects:  C  Mathematical and Quantitative Methods > C2  Single Equation Models; Single Variables > C23  Models with Panel Data; Longitudinal Data; Spatial Time Series C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models; Multiple Variables > C33  Models with Panel Data; Longitudinal Data; Spatial Time Series 
Item ID:  26767 
Depositing User:  Trevor Breusch 
Date Deposited:  17. Nov 2010 12:42 
Last Modified:  13. Feb 2013 00:25 
References:  Baltagi, B., G. Bresson, and A. Pirotte (2003). Fixed effects, random effects or Hausman–Taylor? A pretest estimator. Economics Letters 79 (3), 361–369. Belke, A. and J. Spies (2008). Enlarging the EMU to the east: What effects on trade? Empirica 35 (4), 369–89. Bound, J., D. Jaeger, and R. Baker (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 (430), 443–50. Breusch, T., G. Mizon, and P. Schmidt (1989). Efficient estimation using panel data.Econometrica 57 (3), 695–700. Caporale, G., C. Rault, R. Sova, and A. Sova (2009). On the bilateral trade effects of free trade agreements between the EU15 and the CEEC4 countries. Review of World Economics 145 (2), 189–206. Davidson, R. and J. G. MacKinnon (1993). Estimation and Inference in Econometrics. Oxford University Press. Feldstein, M. (1974). Errors in variables: A consistent estimator with smaller MSE in finite samples. Journal of the American Statistical Association 69 (348), 990–96. Green, E. and W. Strawderman (1991). A JamesStein type estimator for combining unbiased and possibly biased estimators. Journal of the American Statistical Association 86 (416), 1001–06. Han, C. and P. Schmidt (2001). The asymptotic distribution of the instrumental variable estimators when the instruments are not correlated with the regressors. Economics Letters 74 (1), 61–66. Hausman, J. and W. Taylor (1981). Panel data and unobservable individual effects. Econometrica 49 (6), 1377–98. Hoeting, J., D. Madigan, A. Raftery, and C. Volinsky (1999). Bayesian model averaging: A tutorial. Statistical Science 14 (4), 382–401. James, W. and C. Stein (1961). Estimation with quadratic loss. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1, pp. 361–79. University of California Press. Kazimi, C. and D. Brownstone (1999). Bootstrap confidence bands for shrinkage estimators. Journal of Econometrics 90 (1), 99–127. Krogstrup, S. and S. W¨alti (2008). Do fiscal rules cause budgetary outcomes? Public Choice 136 (1), 123–138. Mittelhammer, R. and G. Judge (2005). Combining estimators to improve structural model estimation and inference under quadratic loss. Journal of Econometrics 128 (1), 1–29. Mitze, T. (2009). Endogeneity in panel data models with timevarying and timefixed regressors: to IV or not IV? Ruhr Economic Paper No. 83. Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica 46 (1), 69–85. Plümper, T. and V. Troeger (2007a). Efficient estimation of timeinvariant and rarely changing variables in finite sample panel analyses with unit fixed effects. Political Analysis 15 (2), 124–39. Plümper, T. and V. Troeger (2007b). xtfevd.ado version 2.00 beta. Accessed from http://www.polsci.org/pluemper/xtfevd.ado. Wong, K. (1997). Effects on inference of pretesting the exogeneity of a regressor. Economics Letters 56 (3), 267–71. Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. The MIT Press. 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/26767 
Available Versions of this Item

On the fixedeffects vector decomposition. (deposited 18. Mar 2010 18:20)
 On the fixedeffects vector decomposition. (deposited 17. Nov 2010 12:42) [Currently Displayed]