Information Theoretic Approaches to Inference in Moment Condition Models
AbstractThis paper develops a variant of one-step efficient GMM based on the KLIC rather than empirical likelihood. As in other one-step methods, the authors introduce M (the number of moments) auxiliary 'tilting' parameters which are used to construct a reweighting of the data so that the reweighted sample obeys all the moment conditions at the parameter estimates. Parameter and overidentification tests can be recast in terms of these tilting parameters; such tests are often startlingly more effective than their conventional counterparts. These performance differences cannot be completely explained by the leading terms of the statistics' asymptotic expansions.
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Bibliographic InfoArticle provided by Econometric Society in its journal Econometrica.
Volume (Year): 66 (1998)
Issue (Month): 2 (March)
Other versions of this item:
- Guido W Imbens, Phillip Johnson & Richard H Spady, . "Information theoretic approaches to inference in moment condition model," Economics Papers W12., Economics Group, Nuffield College, University of Oxford.
- Guido W. Imbens & Phillip Johnson & Richard H. Spady, 1995. "Information Theoretic Approaches to Inference in Moment Condition Models," Harvard Institute of Economic Research Working Papers 1736, Harvard - Institute of Economic Research.
- Imbens, G.W. & Johnson, P. & Spady, R.H., 1995. "Information Theoretic Approaches to Inference in Movement Condition Models," Economics Papers 99, Economics Group, Nuffield College, University of Oxford.
- Guido W. Imbens & Phillip Johnson & Richard H. Spady, 1995. "Information Theoretic Approaches to Inference in Moment Condition Models," NBER Technical Working Papers 0186, National Bureau of Economic Research, Inc.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Working Paper Series, Macroeconomic Issues
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- Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-66, July.
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- Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
- Newey, Whitney K. & McFadden, Daniel, 1986. "Large sample estimation and hypothesis testing," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 36, pages 2111-2245 Elsevier.
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