Title: A robust generalised maximum entropy estimator for ill-posed estimation problems

Authors: Graeme J. Doole

Addresses: Waikato Management School, Department of Economics, University of Waikato, Private Bag 3105, Hamilton, New Zealand

Abstract: The generalised maximum entropy (GME) estimator provides a flexible means of information recovery from ill-posed estimation problems. However, coefficient estimates are sensitive to the exogenous support bounds defined for coefficient and error terms. This paper describes a new estimator that identifies informative support bounds, prior to the implementation of GME regression. These bounds are estimated using interval-valued mathematical programming in a way that is data-based, replicable, and robust. The superiority of the new estimator over various alternatives is demonstrated with a series of non-trivial Monte Carlo simulations involving different degrees of multicollinearity, sample sizes, and error structures.

Keywords: maximum entropy; support bounds; ill-posed problems; multicollinearity; low sample size; interval-valued optimisation.

DOI: 10.1504/IJCEE.2018.091044

International Journal of Computational Economics and Econometrics, 2018 Vol.8 No.2, pp.129 - 143

Available online: 23 Feb 2018

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