Sermpinis, G. , Stasinakis, C., Theofilatos, K. and Karathanasopoulos, A. (2014) Inflation and unemployment forecasting with genetic support vector regression. Journal of Forecasting, 33(6), pp. 471-487. (doi: 10.1002/for.2296)
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Abstract
In this paper a hybrid genetic algorithm–support vector regression (GA-SVR) model in economic forecasting and macroeconomic variable selection is introduced. The proposed algorithm is applied to the task of forecasting US inflation and unemployment. GA-SVR genetically optimizes the SVR parameters and adapts to the optimal feature subset from a feature space of potential inputs. The feature space includes a wide pool of macroeconomic variables that might affect the two series under study. The forecasting performance of GA-SVR is benchmarked with a random walk model, an autoregressive moving average model, a moving average convergence/divergence model, a multi-layer perceptron, a recurrent neural network and a genetic programming algorithm. In terms of our results, GA-SVR outperforms all benchmark models and provides evidence on which macroeconomic variables can be relevant predictors of US inflation and unemployment in the specific period under study.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Stasinakis, Professor Charalampos and Sermpinis, Professor Georgios |
Authors: | Sermpinis, G., Stasinakis, C., Theofilatos, K., and Karathanasopoulos, A. |
College/School: | College of Social Sciences > Adam Smith Business School > Accounting and Finance College of Social Sciences > Adam Smith Business School > Economics |
Journal Name: | Journal of Forecasting |
Publisher: | John Wiley & Sons Ltd. |
ISSN: | 0277-6693 |
ISSN (Online): | 1099-131X |
Copyright Holders: | Copyright © 2014 John Wiley & Sons Ltd. |
First Published: | First published in Journal of Forecasting 33(6):471-487 |
Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher. |
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