Enhanced Bayesian Neural Networks for Macroeconomics and Finance

In recent decades, statistical agencies, governmental institutions and central banks increasingly collect vast datasets. Practitioners and academics rely on these datasets to form forecasts about the future, efficiently tailor policies or improve decisions at the corporate level. However, this abundance of data also gives rise to the curse of dimensionality and questions related to separating signal (i.e., extracting information from important covariates) from noise (i.e., covariates which do not convey meaningful information) are key for carrying out precise inference. Fortunately, the recent literature on statistical and econometric modeling in high dimensions using regularization-based techniques offers a range of solutions (see, e.g., Carvalho et al., 2010; Bhattacharya and Dunson, 2011; Griffin and Brown, 2013; Belmonte et al., 2014; Huber et al., 2021). One key shortcoming, however, is that these models often assume linearity between a given response variable (or in general a vector of responses) and a possibly huge panel of covariates. The reason for this is simplicity in estimation and interpretation. Apart from these very general reasons, allowing for arbitrary functional relations in the conditional mean introduces substantial conceptual challenges.