DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS

Hyungsik Moon () and Martin Weidner

Econometric Theory, 2017, vol. 33, issue 1, 158-195

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 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.

Date: 2017
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Citations: View citations in EconPapers (105)

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Related works:
Working Paper: Dynamic linear panel regression models with interactive fixed effects (2014)
Working Paper: Dynamic linear panel regression models with interactive fixed effects (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:33:y:2017:i:01:p:158-195_00

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