Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We compare homoskedastic and heteroskedastic mixed frequency (MF) vector autoregression and Bayesian additive regression tree (BART) models to assess their performance in predicting tail risk at short horizons. MF-BART is a nonlinear state space model, and we discuss approximation-based approaches to devise a computationally efficient estimation algorithm. The models are applied in an out-of-sample exercise for quarterly and monthly macroeconomic variables in Italy. The proposed econometric refinements yield improvements in predictive accuracy.