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Nowcasting with Mixed Frequency Data Using Gaussian Processes

Author

Listed:
  • Niko Hauzenberger
  • Massimiliano Marcellino
  • Michael Pfarrhofer
  • Anna Stelzer

Abstract

We develop Bayesian machine learning methods for mixed data sampling (MIDAS) regressions. This involves handling frequency mismatches and specifying functional relationships between many predictors and the dependent variable. We use Gaussian processes (GPs) and compress the input space with structured and unstructured MIDAS variants. This yields several versions of GP-MIDAS with distinct properties and implications, which we evaluate in short-horizon now- and forecasting exercises with both simulated data and data on quarterly US output growth and inflation in the GDP deflator. It turns out that our proposed framework leverages macroeconomic Big Data in a computationally efficient way and offers gains in predictive accuracy compared to other machine learning approaches along several dimensions.

Suggested Citation

  • Niko Hauzenberger & Massimiliano Marcellino & Michael Pfarrhofer & Anna Stelzer, 2024. "Nowcasting with Mixed Frequency Data Using Gaussian Processes," Papers 2402.10574, arXiv.org, revised Sep 2024.
    • Handle: RePEc:arx:papers:2402.10574
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    References listed on IDEAS

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    1. Kastner, Gregor & Frühwirth-Schnatter, Sylvia, 2014. "Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 408-423.
    2. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    3. JÖrg Breitung & Christoph Roling, 2015. "Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 588-603, November.
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