Nonparametric mixed frequency monitoring macro-at-risk
比较了同方差和异方差混合频率向量自回归与贝叶斯加性回归树模型在短期尾部风险预测中的表现，并应用于意大利宏观经济变量，发现提出的计量改进提高了预测精度。
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.