Empirical Evidence Concerning the Finite Sample Performance of EL-Type Structural Equation Estimation and Inference Methods

Ron Mittelhammer (), George Judge () and Ronald Schoenberg ()

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: This paper presents empirical evidence concerning the finite sample performance of conventional and generalized empirical likelihood-type estimators that utilize instruments in the context of linear structural models characterized by endogenous explanatory variables. There are suggestions in the literature that traditional and non-traditional asymptotically efficient estimators based on moment equations may, for the relatively small sample sizes usually encountered in econometric practice, have relatively large biases and/or variances and provide an inadequate basis for estimation and inference. Given this uncertainty we use a range of data sampling processes and Monte Carlo sampling procedures to accumulate finite sample empirical evidence concerning these questions for a family of generalized empirical likelihood-type estimators in comparison to conventional 2SLS and GMM estimators. Solutions to EL-type empirical momentconstrained optimization problems present formidable numerical challenges. We identify effective optimization algorithms for meeting these challenges.

Keywords: Unbiased moment based estimation and inference; empirical likelihood; empirical exponential likelihood; semiparametric models; conditional estimating equations; finite sample bias and precision; squared error loss; instrumental conditioning variables (search for similar items in EconPapers)
Date: 2003-01-01
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Citations: View citations in EconPapers (16)

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Working Paper: Empirical Evidence Concerning the Finite Sample Performance of EL-Type Structural Equation Estimation and Inference Methods (2003)
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