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Dynamic linear panel regression models with interactive fixed effects

Listed author(s):
  • Hyungsik Roger Moon

    (Institute for Fiscal Studies and USC)

  • Martin Weidner

    ()

    (Institute for Fiscal Studies and cemmap and UCL)

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. A bias corrected least squares estimator is provided. We also present 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. Supplementary material for this paper is available here.

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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP47/14.

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Date of creation: 22 Dec 2014
Handle: RePEc:ifs:cemmap:47/14
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