Dynamic Linear Panel Regression Models with Interactive Fixed Effects

Hyungsik Roger Moon and Martin Weidner

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Abstract: 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 chi-squared distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.

Date: 2026-05
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Published in Econometric Theory 33 (2017) 158-195

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2605.00612

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