Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/104795 
Autor:innen: 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
HWWI Research Paper No. 157
Verlag: 
Hamburgisches WeltWirtschaftsInstitut (HWWI), Hamburg
Zusammenfassung: 
Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, such models easily become very complex and difficult to estimate. The aim of this study is to investigate whether simple nonlinear extensions of autoregressive processes are able to provide more accurate forecasting results than linear models. Therefore, simple autoregressive processes are extended by means of nonlinear transformations (quadratic, cubic, trigonometric, exponential functions) of lagged time series observations and autoregression residuals. The proposed forecasting models are applied to a large set of macroeconomic and financial time series for 10 European countries. Findings suggest that such models, including nonlinear transformation of lagged autoregression residuals, are somewhat able to provide better forecasting results than simple linear models. Thus, it may be possibile to improve the forecasting accuracy of linear models by including nonlinear components.
Schlagwörter: 
nonlinear models
forecasting
transformations
JEL: 
C22
C53
C51
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
814.46 kB





Publikationen in EconStor sind urheberrechtlich geschützt.