Using a Fast Elitist Non-Dominated Genetic Algorithm on Multiobjective Programming for Quarterly Disaggregation of the Gross Domestic Product

  • Raïmi Aboudou Essessinou Institute of Mathematics and Physical Sciences, University of Abomey-Calavi (UAC), and Department of Statistics an Economics Studies at National Institute of Statistics and Economic Analysis
  • Guy Degla

Abstract

This research paper we use a fast elitist multiobjective genetic algorithm to solve the new approach that we propose to quarterly disaggregating of the Gross Domestic Product (GDP) by multiobjective programming. Thus, the quarterly disaggregation of the GDP is described as a quadratic multiobjective programming problem that generalizes Denton's proportional method. The proposed approach has the advantage reduce to one the number of optimization programs to be solved. Our proposed method can be applied to the national accounts of any country that has adopted the National Accounting System. The simulation results are compared to those obtained using Denton’s proportional method and these results revealed the overall performance of the multiobjective programming approach for the quarterly disaggregation of GDP. Our approach is more suitable for taking into account the links between branches of national accounts, in terms of volumes and prices of products demanded during the production process. Also, it reduces forecast error and volatility of quarterly GDP. Besides, it is worth noting that our method is a usfull step for data processing such as chain-linked measures, overlap growth techniques, seasonal adjustment and calendar effects adjustment, in time series and econometrics analysis.
Published
Feb 21, 2020
How to Cite
ESSESSINOU, Raïmi Aboudou; DEGLA, Guy. Using a Fast Elitist Non-Dominated Genetic Algorithm on Multiobjective Programming for Quarterly Disaggregation of the Gross Domestic Product. European Journal of Engineering and Formal Sciences, [S.l.], v. 4, n. 1, p. 24-45, feb. 2020. ISSN 2601-6311. Available at: <http://journals.euser.org/index.php/ejef/article/view/4631>. Date accessed: 11 aug. 2020. doi: http://dx.doi.org/10.26417/ejef.v4i1.p24-45.