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Robust Efficient Method of Moments

Fabio Trojani

Swiss Finance Institute; University of Lugano

Claudio Ortelli

University of Lugano

September 2002

This paper focuses on the robust Efficient Method of Moments (EMM) estimation of a general parametric stationary process and proposes a broad framework for constructing robust EMM statistics in this context. This extends the application field of robust statistics to very general time series settings, including situations where the structural and the auxiliary models in the EMM estimating equations are different, models with latent non linear dynamics, and models where no closed form expressions for the robust pseudo score of the given EMM auxiliary model are available. We characterize the local robustness properties of EMM estimators for time series by computing the corresponding influence functions and propose two versions of a robust EMM (REMM) estimator with bounded IF. Two algorithms by which the two versions of a REMM estimator can be implemented are presented. We then show by Monte Carlo simulation that our REMM estimators are very successful in controlling for the asymptotic bias under model misspecification while maintaining a high efficiency under the ideal structural model.

Number of Pages in PDF File: 40

Keywords: Efficient Method of Moments, Indirect Inference, Influence Function, Robust Estimation, Robust Statistics

JEL Classification: C1, C13, C14, C15, C22

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Date posted: October 16, 2002  

Suggested Citation

Trojani, Fabio and Ortelli, Claudio, Robust Efficient Method of Moments (September 2002). Available at SSRN: http://ssrn.com/abstract=335700 or http://dx.doi.org/10.2139/ssrn.335700

Contact Information

Fabio Trojani (Contact Author)
Swiss Finance Institute ( email )
Via G. Buffi 13
Lugano, CH-6900
HOME PAGE: http://www.people.lu.unisi.ch/trojanif

University of Lugano ( email )
Faculty of Economics
Via Buffi 13
Lugano, 6900
HOME PAGE: http://www.people.lu.unisi.ch/trojanif
Claudio Ortelli
University of Lugano ( email )
via G. Buffi 13
Lugano, 6904
Feedback to SSRN

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Citations:  12
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