Macroeconomic Forecasting with Fractional Factor Models
Econometrics
2020-05-12 v1
Abstract
We combine high-dimensional factor models with fractional integration methods and derive models where nonstationary, potentially cointegrated data of different persistence is modelled as a function of common fractionally integrated factors. A two-stage estimator, that combines principal components and the Kalman filter, is proposed. The forecast performance is studied for a high-dimensional US macroeconomic data set, where we find that benefits from the fractional factor models can be substantial, as they outperform univariate autoregressions, principal components, and the factor-augmented error-correction model.
Keywords
Cite
@article{arxiv.2005.04897,
title = {Macroeconomic Forecasting with Fractional Factor Models},
author = {Tobias Hartl},
journal= {arXiv preprint arXiv:2005.04897},
year = {2020}
}