Generalized Moment Based Estimation and Inference

George Judge () and Marco Van_Akkeren
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Marco Van_Akkeren: University of California

No 73, Econometric Society World Congress 2000 Contributed Papers from Econometric Society

Abstract: We extend the empirical likelihood method of estimation and inference proposed by Owen and others and demonstrate how it may be used in a general linear model context and to mitigate the impact of an ill-conditioned design matrix. A dual loss information theoretic estimating function is used along with extended moment conditions to yield a data based estimator that has the usual consistency and asymptotic normality results. Limiting chi-square distributions may be used to obtain hypothesis test or confidence intervals. The estimator appears to have excellent finite sample properties under a squared error loss measure.

Date: 2000-08-01
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Journal Article: Generalized moment based estimation and inference (2002)
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