Article

Forecasting With Nonlinear Time Series Models

Anders Bredahl Kock and Timo Teräsvirta

in The Oxford Handbook of Economic Forecasting

Published in print July 2011 | ISBN: 9780195398649
Published online September 2012 | | DOI: http://dx.doi.org/10.1093/oxfordhb/9780195398649.013.0004

Series: Oxford Handbooks

 Forecasting With Nonlinear Time Series Models

More Like This

Show all results sharing these subjects:

  • Economics
  • Econometric and Statistical Methods and Methodology: General

GO

Show Summary Details

Preview

This article considers nonlinear forecasting models, such as switching-regime models. These models are typically “small” compared to vector autoregressive and factor models, being either univariate or single-equation models, but tend to nest a linear relationship and so invite an assessment of whether allowing for nonlinearity improves forecast accuracy. The article is organized as follows. Section 2 considers a number of parametric time series models. Some universal approximators, including neural network models, are studied in Section 3. Forecasting several periods ahead with nonlinear models is the topic of Section 4. Forecasting with chaotic systems is briefly considered in Section 5. Comparisons of linear and nonlinear forecasts of economic time series are discussed in Section 6, and studies comprising a large number of series are discussed in Section 7. Section 8 contains a limited forecast accuracy comparison between recursive and direct forecasts. Final remarks and suggestions for further reading can be found in Section 9.

Keywords: nonlinear forecasting models; economic forecasting; neural network models; chaotic systems; nonlinear forecasts

Article.  11055 words. 

Subjects: Economics ; Econometric and Statistical Methods and Methodology: General

Full text: subscription required

How to subscribe Recommend to my Librarian

Buy this work at Oxford University Press »

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.