@techreport{Terasvirta2005Forecasting,
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.},
address = {Stockholm},
author = {Timo Ter\"{a}svirta},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {C22; C45; C53; 330; Forecast accuracy; forecast comparison; hidden Markov model; neural network; nonlinear modelling; recursive forecast; smooth transition regression; switching regression; Prognoseverfahren; Mathematische Optimierung; Nichtlineares Verfahren},
language = {eng},
number = {598},
publisher = {Ekonomiska Forskningsinst.},
title = {Forecasting economic variables with nonlinear models},
type = {SSE/EFI Working Paper Series in Economics and Finance},
url = {http://hdl.handle.net/10419/56166},
year = {2005}
}
