Summary. During recessionary episodes (such as the global financial crisis or the Covid-19 pandemic) many important macroeconomic and financial quantities experience substantial shifts in the volatility process, while during tranquil periods the shock volatilities remain relatively stable. This project sets forth a research agenda to develop and apply more flexible volatility models for macroeconomic and financial data. These methods will be also useful for practitioners working on emerging market economies. These countries often feature short time series which are subject to several structural breaks. The methods we propose in this project are exactly designed for such situations where time series exhibit substantial shifts in the variance. We will illustrate the models developed in this project through various now- and forecasting applications. Apart from focusing on predictions, our models can be used to produce timely indicators of macroeconomic stability. These indicators can be applied to emerging market economies and implemented such that policy institutions have alternative high frequency measures of the overall state of a given economy.

Published papers produced as part of this project.

• Gaussian process VARs and macroeconomic uncertainty.
• Dynamic shrinkage priors for large TVP regressions using scalable MCMC methods.
• Bayesian neural networks for macroeconomic analysis.
• Sparse time-varying parameter VECMs with an application to modeling electricity prices.
• Predictive density combination using Bayesian machine learning.
• High-frequency and heteroskedasticity identification in multicountry models: Revisiting spillovers of monetary shocks.
• Machine learning the macroeconomic effects of financial shocks.

Working papers completed as part of this project.

• Bayesian modeling of TVP-VARs using regression trees (conditionally accepted, Annals of Applied Statistics).
• Bayesian modelling of VAR precision matrices using stochastic block networks.
• Monetary policy and the joint distribution of income and wealth: The heterogeneous case of the euro area.
• Coarsened Bayesian VARs: Correcting BVARs for incorrect specification.
• A Bayesian Gaussian process dynamic factor model.
• Direct Gaussian process predictive regressions with mixed frequency data.

Presentations by project members at international conferences for disseminating results.

• Robust Bayesian VARs through power posteriors presented by Tobias Scheckel at the 16th RCEA Bayesian Econometrics Workshop 2026 (Madrid).
• Stochastic block network vector autoregressions presented by Tobias Scheckel at CFE 2023.
• The international effects of central bank information shocks presented by Anna Stelzer at ICMAIF 2023.
• Nonparametric methods for measuring asymmetries in monetary policy transmission presented by Anna Stelzer at CFE 2022.

Posters presented by project members at international conferences.

• A Bayesian Gaussian process dynamic factor model
• When risk rises: Geopolitical risk and the transmission of global shocks

Activities. Research visit to the International Institute for Applied Systems Analysis (IIASA), Laxenburg, 20–30 June 2026, and workshop “How to keep the economy dancing when the music stops?” at IIASA, 23 June 2026, organised with G. Figus.

Activities. Two workshops at the University of Strathclyde in June 2025, organised with G. Figus under this funding: “Horizon Europe: Practical experience from a case study LAMASUS” and “Applied Bayesian econometrics workshop”.

Activities. Workshop “Recent advances in nowcasting and forecasting” (University of Strathclyde, November 2024), organised with G. Koop, S. McIntyre, and P. Wu under this funding.

Summary. Appropriate communication strategies from central banks are considered to be important tools for the effective implementation of monetary policy. Especially since the financial crisis, where many central banks were tied to the zero lower bound, good communication is essential to compensate for this limitation. We analyze central bank speeches spanning more than five decades to examine different communication strategies and their effects on macroeconomic indicators, taking into account differences in time, region and actors. The project includes the three main contributions. First, building on recently developed text analysis models, we develop extensions and adaptations so that they are particularly suitable for central bank speeches. Second, we apply the existing and newly developed models to a new data set that contains more than $16,000$ speeches by central bankers, which are available on the BIS website. Third, we use covariates derived from the text analysis to fit VARs to these covariates as well as macroeconomic indicators and thus carry out a structural analysis of a communication shock and to check whether this improves the predictive quality for macroeconomic indicators.