OeNB-Freitagsseminar with Michael Pfarrhofer

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Nowcasting with mixed-frequency data using Gaussian processes

OeNB-Freitagsseminar with Michael Pfarrhofer, Vienna University of Economics and Business
We propose and discuss Bayesian machine learning methods for mixed data sampling (MIDAS) regressions. This involves handling frequency mismatches with restricted and unrestricted MIDAS variants and specifying functional relationships between many predictors and the dependent variable. We use Gaussian processes (GP) and Bayesian additive regression trees (BART) as flexible extensions to linear penalized estimation. In a nowcasting and forecasting exercise we focus on quarterly US output growth and inflation in the GDP deflator. The new models leverage macroeconomic Big Data in a computationally efficient way and offer gains in predictive accuracy along several dimensions.

Date
Friday, March 15, 2024 | Start: 11:00 a.m. | End: 12:30 p.m.

Venue
The event is planned both, online via Webex and onsite at the Oesterreichische Nationalbank, Otto-Wagner-Platz 3, 1090 Vienna, Veranstaltungssaal.

Please register by March 13, 2024, at the latest.

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