Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data

César Alonso‐Borrego · Manuel Arellano

613 citations0/0 files storedJournal of Business and Economic Statistics

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Spatial and Panel Data Analysis Monetary Policy and Economic Impact Advanced Causal Inference Techniques

Abstract

We discuss the estimation of linear panel-data models with sequential moment restrictions using symmetrically normalized generalized method of moments (GMM) estimators (SNM) and limited information maximum likelihood (LIML) analogues. These estimators are asymptotically equivalent to standard GMM but are invariant to normalization and tend to have a smaller finite-sample bias, especially when the instruments are poor. We study their properties in relation to ordinary GMM and minimum distance estimators for AR(1) models with individual effects by mean of simulations. Finally, as empirical illustrations, we estimate by SNM and LIML employment and wage equations using panels of U.K. and Spanish firms.

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References

1991Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations 1998Initial conditions and moment restrictions in dynamic panel data models 1995Another look at the instrumental variable estimation of error-components models 1995Initial conditions and moment restrictions in dynamic panel data models 1995Journal of the American Statistical Association 1988Estimating Vector Autoregressions with Panel Data 1982Multivariate regression models for panel data 1996Finite-Sample Properties of Some Alternative GMM Estimators

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