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

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  • Graeme J. Doole

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.

Suggested Citation

  • Graeme J. Doole, 2018. "A robust generalised maximum entropy estimator for ill-posed estimation problems," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 8(2), pages 129-143.
    • Handle: RePEc:ids:ijcome:v:8:y:2018:i:2:p:129-143
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