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The Cumulant Generating Function Estimation Method

Implementation and Asymptotic Efficiency

Published online by Cambridge University Press:  11 February 2009

John L. Knight  and
Stephen E. Satchell
John L. Knight
Affiliation:
University of Western Ontario
Stephen E. Satchell
Affiliation:
Trinity College
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Abstract

This paper deals with the use of the empirical cumulant generating function to consistently estimate the parameters of a distribution from data that are independent and identically distributed (i.i.d.). The technique is particularly suited to situations where the density function is unknown or unbounded in parameter space. We prove asymptotic equivalence of our technique to that of the empirical characteristic function and outline a six-step procedure for its implementation. Extensions of the approach to non-i.i.d. situations are considered along with a discussion of suitable applications and a worked example.

Information

Type
Articles
Information
Econometric Theory , Volume 13 , Issue 2 , April 1997 , pp. 170 - 184
Copyright
Copyright © Cambridge University Press 1997

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References

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