Richard Schnorrenberger

Passionate researcher specializing in time series forecasting in data-rich environments using machine learning methods. With a solid background in collaborative quantitative research, I excel in data analysis and visualization to effectively communicate scientific insights. Driven by a passion for advancing scientific knowledge, I thrive in team-oriented settings.

• Time series forecasting and macroeconomic nowcasting
• Machine learning methods and non-traditional datasets
• Bayesian dynamic modeling and Bayesian VARs
• Yield curve modeling and multivariate stochastic volatility
• Financial econometrics

• Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany, CESifo/ifo Junior Workshop on Big Data, Munich, 2023, Inflation: Drivers and Dynamics Conference 2023 [Poster Session], Federal Reserve Bank of Cleveland and the ECB, Frankfurt, 2023, Statistische Woche 2023, Dortmund, 2023, 23rd IWH-CIREQ-GW Macroeconometric Workshop on Inflation, Halle, 2022
• Harnessing machine learning for real-time inflation nowcasting, 2nd Annual Conference of the Brazilian Central Bank, Brasília, 2024, 43rd International Symposium on Forecasting, Charlottesville, Virginia, 2023, 29th Conference on Computing in Economics and Finance, Nice, France, 2023
• Bond portfolio optimization in turbulent times: A dynamic Nelson-Siegel approach with Wishart stochastic volatility, 12th European Seminar on Bayesian Econometrics [Poster Session], Salzburg, 2022, 42nd International Symposium on Forecasting, Oxford, England, 2022, 15th International Conference on Computational and Financial Econometrics, London, England, 2021

• Science Prize 2025, Deutsche Bundesbank, Hamburg, Mecklenburg-Vorpommern und Schleswig-Holstein, 2025
• CNPq Master's Scholarship, 2015 - 2017, Best Student Award, 2015 graduate cohort
• DAAD Scholarship, 2017, German winter course at the University of Cologne
• CFA Research Challenge Brazil, 2013, Top 3

• Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany, ECB Working Paper Series, No 2930, 2024
• Wie Haushaltsscannerdaten bei der Inflationsprognose helfen, Deutsche Bundesbank Research Brief, 01/30/24
• Real-time food price inflation in Germany in light of the Russian invasion of Ukraine, VoxEU.org, 06/24/22
• Harnessing Machine Learning for Real-Time Inflation Nowcasting, De Nederlandsche Bank Working Paper, No 806, 2024
• Forecasting HICP package holidays with forward-looking booking data, Deutsche Bundesbank Technical Paper, 04/2024, 2024

• Barcelona GSE Macroeconometrics Summer School, 2019, Advanced Bayesian Time Series Methods
• Kiel Institute for the World Economy, Advanced Studies Program, 08/01/17, 08/01/18, Monetary Economics, Time Series Forecasting, Financial Crisis, Big Data in International Macroeconomics and Finance