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.
- Factor models
- Multiscale principal components