Forecasting Using Functional Coefficients Autoregressive Models
AbstractThe use of linear parametric models for forecasting economic time series is widespread among practitioners, in spite of the fact that there is a large evidence of the presence of non-linearities in many of such time series. However, the empirical results stemming from the use of non-linear models are not always as good as expected. This has been sometimes associated to the difficulty in correctly specifying a non-linear parametric model. I this paper I cope with this issue by using a more general non-parametric approach, which can be used both as a preliminary tool for aiding in specifying a suitable parametric model and as an autonomous modelling strategy. The results are promising, in that the non-parametric approach achieve a good forecasting record for a considerable number of series.
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Bibliographic InfoPaper provided by ISTAT - Italian National Institute of Statistics - (Rome, ITALY) in its series ISAE Working Papers with number 98.
Length: 29 pages
Date of creation: Jun 2008
Date of revision:
Non-linear Time-Series Models; Non-Parametric Models.;
Other versions of this item:
- Bruno, Giancarlo, 2008. "Forecasting Using Functional Coefficients Autoregressive Models," MPRA Paper 42335, University Library of Munich, Germany.
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-08-06 (All new papers)
- NEP-ECM-2008-08-06 (Econometrics)
- NEP-ETS-2008-08-06 (Econometric Time Series)
- NEP-FOR-2008-08-06 (Forecasting)
- NEP-ORE-2008-08-06 (Operations Research)
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