Forecasting US unemployment with radial basis neural networks, kalman filters and support vector regressions

Stasinakis, C. , Sermpinis, G. , Theofilatos, K. and Karathanasopoulos, A. (2016) Forecasting US unemployment with radial basis neural networks, kalman filters and support vector regressions. Computational Economics, 47(4), pp. 569-587. (doi: 10.1007/s10614-014-9479-y)

Abstract

This study investigates the efficiency of the radial basis function neural networks in forecasting the US unemployment and explores the utility of Kalman filter and support vector regression as forecast combination techniques. On one hand, an autoregressive moving average model, a smooth transition autoregressive model and three different neural networks architectures, namely a multi-layer perceptron, recurrent neural network and a psi sigma network are used as benchmarks for our radial basis function neural network. On the other hand, our forecast combination methods are benchmarked with a simple average and a least absolute shrinkage and selection operator. The statistical performance of our models is estimated throughout the period of 1972–2012, using the last 7 years for out-of-sample testing. The results show that the radial basis function neural network statistically outperforms all models’ individual performances. The forecast combinations are successful, since both Kalman filter and support vector regression techniques improve the statistical accuracy. Finally, support vector regression is found to be the superior model of the forecasting competition. The empirical evidence of this application are further validated by the use of the modified Diebold–Mariano test.

• Item Type: Articles
• Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/s10614-014-9479-y
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Stasinakis, Professor Charalampos and Sermpinis, Professor Georgios
• Authors: Stasinakis, C., Sermpinis, G., 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: Computational Economics
• Publisher: Springer US
• ISSN: 0927-7099
• ISSN (Online): 1572-9974
• Copyright Holders: Copyright © Springer Science+Business Media New York 2014
• First Published: First published in Computational Economics 47(4): 569-587
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher