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Bayesian Nowcasting with Mixed Frequency Data Using Gaussian Processes

Author

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

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 MI-DAS 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. Our proposed framework leverages macroeconomic Big Data in a computationally efficient way and offers gains in predictive accuracy along several dimensions.

Suggested Citation

  • Hauzenberger, Niko & Marcellino, Massimiliano & Pfarrhofer, Michael & Stelzer, Anna, 2025. "Bayesian Nowcasting with Mixed Frequency Data Using Gaussian Processes," CEPR Discussion Papers 19965, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:19965
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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