FORECASTING THE COMPOSITION OF DEMAND FOR HIGHER EDUCATION DEGREES BY GENETIC ALGORITHMS

Montserrat Hernández-LÓPEZ and José Juan Cáceres-Hernández
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Montserrat Hernández-LÓPEZ: Departamento de Economía Aplicada y Métodos Cuantitativos, University of La Laguna

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2016, vol. 50, issue 3, 153-172

Abstract: A genetic algorithm is developed to forecast the relative presence of different university studies in the higher education demand in the field of economics and business/management as a whole. A selection operator is defined that assumes that the better the job opportunities associated with a specific university study, the higher the future demand for such a degree. A transition matrix takes other factors into account which may influence on the changes in demand. The proposed algorithm is applied to the original populations of students enrolled on 2005/2006 to 2007/2008 courses. Then, a new algorithm, whose elements are corrected to adjust the forecasts, is applied to obtain the forecast of the demand composition on the 2009/2010 course. This methodological proposal is shown to be able to provide the type of forecast which is very useful in policy making decisions in the recent process of building the European Higher Education Area.

Keywords: higher education; forecasting; genetic algorithms. (search for similar items in EconPapers)
JEL-codes: C63 I23 (search for similar items in EconPapers)
Date: 2016
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