Inflation and unemployment forecasting with genetic support vector regression

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)

94791.pdf - Accepted Version



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
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 (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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