Bayesian Nowcasting with Mixed Frequency Data Using Gaussian Processes

Niko Hauzenberger, Massimiliano Marcellino, Michael Pfarrhofer and Anna Stelzer

No 19965, CEPR Discussion Papers from Centre for Economic Policy Research

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.

JEL-codes: C11 C22 C53 E31 E37 (search for similar items in EconPapers)
Date: 2025-02
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