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DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS
Published online by Cambridge University Press: 10 December 2015
Abstract
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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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Footnotes
This paper is based on an unpublished manuscript of the authors that was circulated under the title “Likelihood Expansion for Panel Regression Models with Factors” but is now completely assimilated by the current paper and Moon & Weidner (2015). We greatly appreciate comments from the participants in the Far Eastern Meeting of the Econometric Society 2008, the SITE 2008 Conference, the All-UC-Econometrics Conference 2008, the July 2008 Conference in Honour of Peter Phillips in Singapore, the International Panel Data Conference 2009, the North American Summer Meeting of the Econometric Society 2009, and from seminar participants at Penn State, UCLA, and USC. We are also grateful for the comments and suggestions of Guido Kuersteiner, Peter Phillips, and anonymous referees. Moon is grateful for the financial support from the NSF via grant SES 0920903 and the faculty development award from USC. Weidner acknowledges support from the Economic and Social Research Council through the ESRC Centre for Microdata Methods and Practice grant RES-589-28-0001.
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