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Nowcasting distributions: a functional MIDAS model

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  • Massimiliano Marcellino
  • Andrea Renzetti
  • Tommaso Tornese

Abstract

We propose a functional MIDAS model to leverage high-frequency information for forecasting and nowcasting distributions observed at a lower frequency. We approximate the low-frequency distribution using Functional Principal Component Analysis and consider a group lasso spike-and-slab prior to identify the relevant predictors in the finite-dimensional SUR-MIDAS approximation of the functional MIDAS model. In our application, we use the model to nowcast the U.S. households' income distribution. Our findings indicate that the model enhances forecast accuracy for the entire target distribution and for key features of the distribution that signal changes in inequality.

Suggested Citation

  • Massimiliano Marcellino & Andrea Renzetti & Tommaso Tornese, 2024. "Nowcasting distributions: a functional MIDAS model," Papers 2411.05629, arXiv.org.
    • Handle: RePEc:arx:papers:2411.05629
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    References listed on IDEAS

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    2. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
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    4. Christian Bayer & Ralph Luetticke & Lien Pham‐Dao & Volker Tjaden, 2019. "Precautionary Savings, Illiquid Assets, and the Aggregate Consequences of Shocks to Household Income Risk," Econometrica, Econometric Society, vol. 87(1), pages 255-290, January.
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