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A wavelet-based multivariate multiscale approach for forecasting
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DOI：10.1016/j.ijforecast.2017.01.007.png)
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Abstract
En 中文
In our increasingly data-rich environment, factor models have become the workhorse approach for modelling and forecasting purposes. However, factors are not observable and have to be estimated. In particular, the space spanned by the unknown factors is typically estimated via principal components. This paper proposes a novel procedure for estimating the factor space, resorting to a wavelet-based multiscale principal component analysis. A Monte Carlo simulation study is used to demonstrate that such an approach may improve both the estimation and the forecasting performances of factor models. The empirical application then illustrates its usefulness for forecasting GDP growth and inflation in the United States. (C) 2017 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Wavelets
Multiscale principal components
Factor models
Forecasting
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