A wavelet approach for factor-augmented forecasting

A wavelet approach for factor-augmented forecasting

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Article ID: iaor201112533
Volume: 30
Issue: 7
Start Page Number: 666
End Page Number: 678
Publication Date: Nov 2011
Journal: Journal of Forecasting
Authors:
Keywords: time series: forecasting methods, simulation: applications
Abstract:

It has been acknowledged that wavelets can constitute a useful tool for forecasting in economics. Through a wavelet multi-resolution analysis, a time series can be decomposed into different timescale components and a model can be fitted to each component to improve the forecast accuracy of the series as a whole. Up to now, the literature on forecasting with wavelets has mainly focused on univariate modelling. On the other hand, in a context of growing data availability, a line of research has emerged on forecasting with large datasets. In particular, the use of factor-augmented models have become quite widespread in the literature and among practitioners. The aim of this paper is to bridge the two strands of the literature. A wavelet approach for factor-augmented forecasting is proposed and put to test for forecasting GDP growth for the major euro area countries. The results show that the forecasting performance is enhanced when wavelets and factor-augmented models are used together.

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