Bounded tilting estimation

The search for one-step alternatives to the generalized method of moment (GMM) has identified broad classes of potential estimators such as generalized empirical likelihoods (GEL), empirical cressie-read (ECR), exponentially tilted empirical likelihood (ETEL), and minimum discrepancy (MD) estimators. While empirical likelihood (EL) dominates other ECR estimators in terms of higher-order asymptotics, it lacks robustness to model misspecification. ETEL was shown to combine higher-order efficiency and robustness to misspecification but demands strong moment generating function existence conditions. We show, both theoretically and via simulations, how to achieve the same goal under weaker moment existence conditions within the class of MD estimators.

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Publication Details

Journal
Econometric Reviews
Published
2024-09-17
DOI
https://doi.org/10.1080/07474938.2024.2396108
Primary Topic
Image and Signal Denoising Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Bounded tilting estimation

Susanne M. Schennach, Oscar Wahlstrom
Econometric Reviews
Image and Signal Denoising Methods
article

Bounded tilting estimation

Susanne M. Schennach, Oscar Wahlstrom
article en

Abstract

The search for one-step alternatives to the generalized method of moment (GMM) has identified broad classes of potential estimators such as generalized empirical likelihoods (GEL), empirical cressie-read (ECR), exponentially tilted empirical likelihood (ETEL), and minimum discrepancy (MD) estimators. While empirical likelihood (EL) dominates other ECR estimators in terms of higher-order asymptotics, it lacks robustness to model misspecification. ETEL was shown to combine higher-order efficiency and robustness to misspecification but demands strong moment generating function existence conditions. We show, both theoretically and via simulations, how to achieve the same goal under weaker moment existence conditions within the class of MD estimators.

Econometric ReviewsVol. 44(2)
Brown University (US)
National Science Foundation
Openalex Percentile: Top 85%
Image and Signal Denoising Methods
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