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Multiple local optima for seemingly unrelated regression models

Multiple local optima for seemingly unrelated regression models This paper provides an example of multiple local maxima to the likelihood function for a seemingly unrelated regression (SUR) model with a cross equation restriction. Since this is the least complex of the various structural equations models (SEM) and since maximum likelihood is often the preferred technique for SEM the problem of multiple local maxima is expected to be pervasive. Keywords: point estimation; maximum likelihood; structural equation model; cross equation restrictions; specification test; multiple local optima; seemingly unrelated regression models. Reference to this paper should be made as follows: Womer, N.K. (2016) `Multiple local optima for seemingly unrelated regression models', Int. J. Computational Economics and Econometrics, Vol. 6, No. 1, pp.44­55. Biographical notes: Norman Keith Womer served as a Dean 2004­2013 and currently as a Professor of Logistics and Operations Management at The University of Missouri ­ St. Louis. Previously, he served at the University of Mississippi, Clemson University, the Air Force Institute of Technology, and the Naval Postgraduate School. He earned the PhD in Economics from Penn State and a BA from Miami University. He has written extensively in the area of cost estimation and project management in the public sector. He teaches in the areas of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

Multiple local optima for seemingly unrelated regression models

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Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2016.073343
Publisher site
See Article on Publisher Site

Abstract

This paper provides an example of multiple local maxima to the likelihood function for a seemingly unrelated regression (SUR) model with a cross equation restriction. Since this is the least complex of the various structural equations models (SEM) and since maximum likelihood is often the preferred technique for SEM the problem of multiple local maxima is expected to be pervasive. Keywords: point estimation; maximum likelihood; structural equation model; cross equation restrictions; specification test; multiple local optima; seemingly unrelated regression models. Reference to this paper should be made as follows: Womer, N.K. (2016) `Multiple local optima for seemingly unrelated regression models', Int. J. Computational Economics and Econometrics, Vol. 6, No. 1, pp.44­55. Biographical notes: Norman Keith Womer served as a Dean 2004­2013 and currently as a Professor of Logistics and Operations Management at The University of Missouri ­ St. Louis. Previously, he served at the University of Mississippi, Clemson University, the Air Force Institute of Technology, and the Naval Postgraduate School. He earned the PhD in Economics from Penn State and a BA from Miami University. He has written extensively in the area of cost estimation and project management in the public sector. He teaches in the areas of

Journal

International Journal of Computational Economics and Econometrics – Inderscience Publishers

Published: Jan 1, 2016

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