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Dynamic linear panel regression models with interactive fixed effects

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  • Hyungsik Roger Moon

    (Institute for Fiscal Studies and USC)

  • Martin Weidner

    (Institute for Fiscal Studies and UCL)

Abstract

We analyze linear panel regression models with interactive fixed effects and predetermined regressors, e.g. lagged-dependent variables. The first order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross sectional dimension and the number of time periods become large. We find that there are two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term, and bias due to predetermined (as opposed to strictly exogenous) regressors. We provide an estimator for the bias and a bias corrected LS estimator for the case where idiosyncratic errors are independent across both panel dimensions. Furthermore, we provide bias corrected versions of the three classical test statistics (Wald, LR and LM test) and show that their asymptotic distribution is a chi-square-distribution. Monte Carlo simulations show that the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes. A supplement to this paper can be downloaded here.

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Bibliographic Info

Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP63/13.

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Date of creation: Dec 2013
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Handle: RePEc:ifs:cemmap:63/13

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