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  • Hyungsik Roger Moon (a1) (a2) and Martin Weidner (a3)

We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example 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 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 a bias-corrected LS estimator. We also present bias-corrected versions of the three classical test statistics (Wald, LR, and LM test) and show their asymptotic distribution is a χ2-distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.

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*Address correspondence to Hyungsik Roger Moon, Department of Economics and USC Dornsife INET, University of Southern California, Los Angeles, CA 90089-0253. e-mail:
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Econometric Theory
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  • EISSN: 1469-4360
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