A robust generalised maximum entropy estimator for ill-posed estimation problems. (2018)

• Record Type: Journal Article
• Authors: Doole, Graeme J.
• Is Part Of: International journal of computational economics and econometrics. Volume 8:Number 2(2018)
• Page Start: 129
• Page End: 143
• Publication Date: 2018
• Subjects: maximum entropy -- support bounds -- ill-posed problems -- multicollinearity -- low sample size -- interval-valued optimisation; Econometrics -- Periodicals; Economics -- Data processing -- Periodicals; 330.01519505
• Languages: English
• ISSNs: 1757-1170

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.