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Unobserved Heterogeneity in Panel Time Series Models

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Author Info

  • Jerry Coakley
  • Ana-Maria Fuertes
  • Ron Smith

    (Department of Economics, Mathematics & Statistics, Birkbeck)

Abstract

Recently, the large T panel literature has emphasized unobserved, time-varying heterogeneity that may stem from omitted common variables or global shocks that affect each individual unit differently. These latent common factors induce cross-section dependence and may lead to inconsistent regression coefficient estimates if they are correlated with the explanatory variables. Moreover if the process underlying these factors is nonstationary, the individual regressions will be spurious but pooling or averaging across individual estimates still permits consistent estimates of a long-run coefficient. The need to tackle both error cross-section dependence and persistent autocorrelation is motivated by evidence of their pervasiveness found in three well-known international finance and macroeconomic examples. A range of estimators is surveyed and their finite sample properties are examined by means of Monte Carlo experiments. These reveal that a mean group version of the common-correlated-effects estimator stands out as the most robust since it is the preferred choice in rather general (non) stationary settings where regressors and errors share common factors and their factor loadings are possibly dependent. Other approaches which perform reasonably well include the two-way fixed effects, demeaned mean group and between estimators but they are less efficient than the common-correlated-effects estimator.

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File URL: http://www.ems.bbk.ac.uk/research/wp/PDF/BWPEF0403.pdf
File Function: First version, 2004
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Bibliographic Info

Paper provided by Birkbeck, Department of Economics, Mathematics & Statistics in its series Birkbeck Working Papers in Economics and Finance with number 0403.

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Length: 25 pages
Date of creation: May 2004
Date of revision:
Handle: RePEc:bbk:bbkefp:0403

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Keywords: factor analysis; global shocks; latent variables;

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References

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  17. Foschi, Paolo & Belsley, David A. & Kontoghiorghes, Erricos J., 2003. "A comparative study of algorithms for solving seemingly unrelated regressions models," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 3-35, October.
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