Economics at your fingertips  

Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions

Charalampos Stasinakis (), Georgios Sermpinis, Konstantinos Theofilatos () and Andreas Karathanasopoulos ()
Additional contact information
Charalampos Stasinakis: Business School
Konstantinos Theofilatos: University of Patras
Andreas Karathanasopoulos: Royal Docks Business School, University of East London

Computational Economics, 2016, vol. 47, issue 4, No 4, 569-587

Abstract: 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.

Keywords: Forecast combinations; Kalman filter; Support vector regression; Unemployment (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-014-9479-y

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

Page updated 2022-11-18
Handle: RePEc:kap:compec:v:47:y:2016:i:4:d:10.1007_s10614-014-9479-y