Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data
The authors discuss the estimation of linear panel-data models with sequential moment restrictions using symmetrically normalized generalized method of moments (SNM) estimators and limited information maximum likelihood (LIML) analogues. These estimators are asymptotically equivalent to standard generalized method of moments (GMM) estimators but are invariant to normalization and tend to have a smaller finite-sample bias, especially when the instruments are poor. The authors study their properties in relation to ordinary GMM and minimum distance estimators for AR(l) models with individual effects by mean of simulations. Finally, as empirical illustrations, they estimate by SNM and LIML employment and wage equations using panels of U.K. and Spanish firms.
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Volume (Year): 17 (1999)
Issue (Month): 1 (January)
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