The empirical saddlepoint likelihood estimator applied to two-step GMM
AbstractThe empirical saddlepoint likelihood (ESPL) estimator is introduced. The ESPL provides improvement over one-step GMM estimators by including additional terms to automatically reduce higher order bias. The first order sampling properties are shown to be equivalent to efficient two-step GMM. New tests are introduced for hypothesis on the model's parameters. The higher order bias is calculated and situations of practical interest are noted where this bias will be smaller than for currently available estimators. As an application, the ESPL is used to investigate an overidentified moment model. It is shown how the model's parameters can be estimated with both the ESPL and a conditional ESPL (CESPL), conditional on the overidentifying restrictions being satisfied. This application leads to several new tests for overidentifying restrictions. Simulations demonstrate that ESPL and CESPL have smaller bias than currently available one-step GMM estimators. The simulations also show new tests for overidentifying restrictions that have performance comparable to or better than currently available tests. The computations needed to calculate the ESPL estimator are comparable to those needed for a one-step GMM estimator.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 15494.
Date of creation: Feb 2009
Date of revision: May 2009
Generalized method of moments estimator; test of overidentifying restrictions; sampling distribution; empirical saddlepoint approximation; asymptotic distribution; higher order bias;
Find related papers by JEL classification:
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
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