Related papers: Nowcasting with Mixed Frequency Data Using Gaussian Processes

Nowcasting with Mixed Frequency Data Using Gaussian Processes

URL: http://arxiv.org/abs/2402.10574v2
Date: Mon, 9 Sep 2024 18:15:19 GMT
Title: Nowcasting with Mixed Frequency Data Using Gaussian Processes
Authors: Niko Hauzenberger, Massimiliano Marcellino, Michael Pfarrhofer, Anna Stelzer,
Abstract summary: We develop machine learning methods for mixed data sampling (MIDAS) regressions. We use Gaussian processes (GPs) and compress the input space with structured and unstructured MIDAS variants.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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.

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