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Dynamic linear panel regression models with interactive fixed effects

Authors: Hyungsik Roger Roger Moon and Martin Weidner
Date: 22 December 2014
Type: cemmap Working Paper, CWP47/14
DOI: 10.1920/wp.cem.2014.4714

Abstract

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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Now published:
Hyungsik Roger Moon and Martin Weidner December 2015, Dynamic linear panel regression models with interactive fixed effects, Journal Article, Cambridge University Press
Previous version:
Hyungsik Roger Roger Moon and Martin Weidner December 2013, Dynamic linear panel regression models with interactive fixed effects, cemmap Working Paper, CWP63/13, IFS

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