12 October 2022 at 10:00AM | online: Karin Klieber will defend her doctoral thesis “Macroeconomic forecasting in the post-covid era” which is supervised by Professor Dr. Florian Huber. You can join the defense on Zoom:  https://us06web.zoom.us/j/81905413268

Title: Enhanced Bayesian Neural Networks for Macroeconomics and Finance

Abstract: We develop Bayesian neural networks (BNNs) that permit to model generic nonlinearities and time variation for (possibly large sets of) macroeconomic and financial variables. From a methodological point of view, we allow for a general specification of networks that combines various activation functions, a possibly very large number of neurons, and stochastic volatility (SV) for the error term. From a computational point of view, we develop fast and efficient estimation algorithms for the general BNNs we introduce. From an empirical point of view, we show both with simulated data and with a set of common macro and financial applications that our BNNs can be of practical use, particularly so for observations in the tails of the cross-sectional or time series distributions of the target variables.