Factor-augmented forecasting in big data

Juhee Bae

International Journal of Forecasting, 2024, vol. 40, issue 4, 1660-1688

Abstract: This paper evaluates the predictive performance of various factor estimation methods in big data. Extensive forecasting experiments are examined using seven factor estimation methods with 13 decision rules determining the number of factors. The out-of-sample forecasting results show that the first Partial Least Squares factor (1-PLS) tends to be the best-performing method among all the possible alternatives. This finding is prevalent in many target variables under different forecasting horizons and models. This significant improvement can be explained by the PLS factor estimation strategy that considers the covariance with the target variable. Second, using a consistently estimated number of factors may not necessarily improve forecasting performance. The greatest predictive gain often derives from decision rules that do not consistently estimate the true number of factors.

Keywords: Factor models; Forecasting; Partial Least Squares (PLS); Dimension reduction; Diffusion index; Big data (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:4:p:1660-1688

DOI: 10.1016/j.ijforecast.2024.02.004

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