Factor-augmented sparse MIDAS regressions with an application to nowcasting

Jad Beyhum and Jonas Striaukas

Papers from arXiv.org

Abstract: This article investigates factor-augmented sparse MIDAS (Mixed Data Sampling) regressions for high-dimensional time series data, which may be observed at different frequencies. Our novel approach integrates sparse and dense dimensionality reduction techniques. We derive the convergence rate of our estimator under misspecification due to the MIDAS approximation error, $\tau$-mixing dependence, and polynomial tails. Our method's finite sample performance is assessed via Monte Carlo simulations. We apply the methodology to nowcasting U.S. GDP growth and demonstrate that it outperforms both sparse regression and standard factor-augmented regression during the COVID-19 pandemic. These findings indicate that the growth through this period was influenced by both idiosyncratic (sparse) and common (dense) shocks. The approach is implemented in the midasml R package, available on CRAN.

Date: 2023-06, Revised 2025-10
New Economics Papers: this item is included in nep-ecm and nep-ets
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Working Paper: Factor-augmented sparse MIDAS regressions with an application to nowcasting (2024)
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