Forecasting Using Functional Coefficients Autoregressive Models

Giancarlo Bruno

MPRA Paper from University Library of Munich, Germany

Abstract: The 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.

Keywords: Non-linear time-series models; non-parametric models (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Date: 2008-06
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Related works:
Working Paper: Forecasting Using Functional Coefficients Autoregressive Models (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:42335

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