Department of Economics and Business Economics

Timo Teräsvirta

Forecasting with nonlinear time series models

Research output: Working paperResearch


  • Rp10 01

    Final published version, 284 KB, PDF-document

  • School of Economics and Management
In this paper, nonlinear models are restricted to mean nonlinear
parametric models. Several such models popular in time series econo-
metrics are presented and some of their properties discussed. This in-
cludes two models based on universal approximators: the Kolmogorov-
Gabor polynomial model and two versions of a simple artificial neural
network model. Techniques for generating multi-period forecasts from
nonlinear models recursively are considered, and the direct (non-recursive)
method for this purpose is mentioned as well. Forecasting with com-
plex dynamic systems, albeit less frequently applied to economic fore-
casting problems, is briefly highlighted. A number of large published
studies comparing macroeconomic forecasts obtained using different
time series models are discussed, and the paper also contains a small
simulation study comparing recursive and direct forecasts in a partic-
ular case where the data-generating process is a simple artificial neural
network model. Suggestions for further reading conclude the paper.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages32
Publication statusPublished - 2010

    Research areas

  • forecast accuracy, Kolmogorov-Gabor, nearest neighbour, neural network, nonlinear regression

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