Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56166
Authors: 
Teräsvirta, Timo
Year of Publication: 
2005
Series/Report no.: 
SSE/EFI Working Paper Series in Economics and Finance 598
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.
Subjects: 
Forecast accuracy
forecast comparison
hidden Markov model
neural network
nonlinear modelling
recursive forecast
smooth transition regression
switching regression
JEL: 
C22
C45
C53
Document Type: 
Working Paper

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