Size Matters: Covariance Matrix Estimation Under the Alternative
AbstractThe purpose of this paper is to investigate, using Monte Carlo methods, whether or not Hall's (2000) centered test of overidentifying restrictions for parameters estimated by Generalized Method of Moments (GMM) is more powerful, once the test is size-adjusted, than the standard test introduced by Hansen (1982). The Monte Carlo evidence shows that very little size-adjusted power is gained over the standard uncentered calculation. Empirical examples using Epstein and Zin (1991) preferences demonstrate that the centered and uncentered tests sometimes lead to different conclusions about model specification.
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Bibliographic InfoPaper provided by Queen's University, Department of Economics in its series Working Papers with number 1091.
Length: 16 pages
Date of creation: Aug 2005
Date of revision:
Size; Power; GMM; Overidentifying restrictions;
Other versions of this item:
- Jason Allen, 2007. "Size matters: covariance matrix estimation under the alternative," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 637-644, November.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
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