Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons and Monte Carlo simulations for the simple AR(1) model demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual first-differenced GMM estimator, and compared to non-linear GMM. The importance of these results is illustrated in an application to the estimation of a labour demand model using company panel data. (c) 2023 Published by Elsevier B.V.
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Institute for Fiscal Studies and Nuffield College, Oxford,OX1 1NF, United KingdomInstitute for Fiscal Studies and Department of Economics, University College London, London WC1E 6BT, United Kingdom
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IVL Swedish Environm Res Inst, Stockholm, Sweden
Univ Bristol, Dept Civil Engn, Bristol, Avon, England
CNDS, Ctr Nat Disaster Sci, Uppsala, SwedenIVL Swedish Environm Res Inst, Stockholm, Sweden
Westerberg, Ida K.
Di Baldassarre, Giuliano
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CNDS, Ctr Nat Disaster Sci, Uppsala, Sweden
Uppsala Univ, Dept Earth Sci, Uppsala, SwedenIVL Swedish Environm Res Inst, Stockholm, Sweden
Di Baldassarre, Giuliano
Beven, Keith J.
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Uppsala Univ, Dept Earth Sci, Uppsala, Sweden
Univ Lancaster, Lancaster Environm Ctr, Lancaster, EnglandIVL Swedish Environm Res Inst, Stockholm, Sweden
Beven, Keith J.
Coxon, Gemma
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Univ Bristol, Sch Geog Sci, Bristol, Avon, EnglandIVL Swedish Environm Res Inst, Stockholm, Sweden
Coxon, Gemma
Krueger, Tobias
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Humboldt Univ, IRI THESys, Berlin, GermanyIVL Swedish Environm Res Inst, Stockholm, Sweden