Macroeconomic Forecasting with Fractional Factor Models

Tobias Hartl

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arXiv · 2005.04897

Macroeconomic Forecasting with Fractional Factor Models

Tobias Hartl

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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.

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Tobias Hartl. 2020-05-11. Macroeconomic Forecasting with Fractional Factor Models. https://arxiv.org/abs/2005.04897

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