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The Economic Research Institute, Stockholm School of Economics SSE/EFI Working Paper Series in Economics and Finance

No 598:
Forecasting economic variables with nonlinear models

Timo Teräsvirta ()

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 discussed.

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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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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