SSE/EFI Working Paper Series in Economics and Finance
Forecasting economic variables with nonlinear models
Abstract: This article is concerned with forecasting from nonlinear
conditional mean models. First, a number of often applied nonlinear
conditional mean models are introduced and their main properties discussed.
The next section is devoted to techniques of building nonlinear models.
Ways of computing multi-step ahead forecasts from nonlinear models are
surveyed. Tests of forecast accuracy in the case where the models
generating the forecasts are nested are discussed. There is a numerical
example, showing that even when a stationary nonlinear process generates
the observations, future obervations may in some situations be better
forecast by a linear model with a unit root. Finally, some empirical
studies that compare forecasts from linear and nonlinear models are
Keywords: Forecast accuracy; forecast comparison; hidden Markov model; neural network; nonlinear modelling; recursive forecast; smooth transition regression; switching regression; (follow links to similar papers)
JEL-Codes: C22; C45; C53; (follow links to similar papers)
55 pages, May 31, 2005, Revised December 29, 2005
This paper has been prepared for Graham Elliott, Clive W.J. Granger and Allan Timmermann (eds.). Handbook of Economic Forecasting. Amsterdam: Elsevier. This version replaces the previous faulty one (references missing).
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Full text versions of the paper:
- This paper is published as:
Teräsvirta, Timo, (2006), 'Forecasting economic variables with nonlinear models' in Elliott, Graham, Clive W.J. Granger and Allan Timmermann (eds.) Handbook of Economic Forecasting, Vol. 1, pages 413-457, Elsevier.
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