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Forecasting economic variables with nonlinear models

Timo Teräsvirta ()

No 598, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

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 (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 (search for similar items in EconPapers)
Date: 2005-05-31, Revised 2005-12-29
Note: 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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Published in Handbook of Economic Forecasting, Elliott, Graham, Granger, Clive W.J., Timmermann, Allan (eds.), 2006, pages 413-457, Elsevier.

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