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)

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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
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Stasinakis, Dr Charalampos and Sermpinis, Dr 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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