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    Unobserved heterogeneity in panel time series models

    Coakley, Jerry and Fuertes, A.-M. and Smith, Ron P. (2004) Unobserved heterogeneity in panel time series models. Working Paper. Birkbeck, University of London, London, UK.

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    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 non-stationary, the individual regressions will be spurious but pooling or averaging across individual estimates still permits consistent estimation of a long-run coefficient. The need to tackle both error cross-section dependence and persistent auto-correlation is motivated by the 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.


    Item Type: Monograph (Working Paper)
    Additional Information: BWPEF 0403
    Keyword(s) / Subject(s): Factor analysis, global shocks, latent variables
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Research Centres and Institutes: Innovation Management Research, Birkbeck Centre for
    Depositing User: Administrator
    Date Deposited: 09 Apr 2019 12:05
    Last Modified: 02 Aug 2023 17:50


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