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Inflation and Unemployment Forecasting with Genetic Support Vector Regression

Michael Breitner (), Christian Dunis, Hans-Jörg Mettenheim, Christopher Neely, Georgios Sermpinis, Georgios Sermpinis, Charalampos Stasinakis, Konstantinos Theofilatos and Andreas Karathanasopoulos
Authors registered in the RePEc Author Service: Georgios Sermpinis

Journal of Forecasting, 2014, vol. 33, issue 6, 471-487

Abstract: 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. Copyright © 2014 John Wiley & Sons, Ltd.

Date: 2014
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