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Information Theoretic Approaches to Inference in Moment Condition Models

35 Pages Posted: 13 Sep 2000  

Guido W. Imbens

Stanford Graduate School of Business

Richard H. Spady

European University Institute - Economics Department (ECO)

Phillip Johnson

Harvard University - Department of Economics

Date Written: October 1995

Abstract

One-step efficient GMM estimation has been developed in the recent papers of Back and Brown (1990), Imbens (1993) and Qin and Lawless (1994). These papers emphasized methods that correspond to using Owen's (1988) method of empirical likelihood to reweight the data so that the reweighted sample obeys all the moment restrictions at the parameter estimates. In this paper we consider an alternative KLIC motivated weighting and show how it and similar discrete reweightings define a class of unconstrained optimization problems which includes GMM as a special case. Such KLIC-motivated reweightings introduce M auxiliary `tilting' parameters, where M is the number of moments; parameter and overidentification hypotheses can be recast in terms of these tilting parameters. Such tests, when appropriately conditioned on the estimates of the original parameters, are often startlingly more effective than their conventional counterparts. This is apparently due to the local ancillarity of the original parameters for the tilting parameters.

Suggested Citation

Imbens, Guido W. and Spady, Richard H. and Johnson, Phillip, Information Theoretic Approaches to Inference in Moment Condition Models (October 1995). NBER Working Paper No. t0186. Available at SSRN: https://ssrn.com/abstract=225095

Guido W. Imbens (Contact Author)

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Richard H. Spady

European University Institute - Economics Department (ECO) ( email )

Villa San Paolo
Via della Piazzuola 43
50133 Florence
Italy

Phillip Johnson

Harvard University - Department of Economics

Littauer Center
Cambridge, MA 02138
United States

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