Inference in Bayesian additive vector autoregressive tree models
نویسندگان
چکیده
Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This assumption might be overly restrictive could have a deleterious impact on forecasting accuracy. As solution we propose combining VAR with Bayesian additive regression tree (BART) models. The resulting vector (BAVART) model is capable of capturing arbitrary nonlinear relations covariates without much input from researcher. Since controlling for heteroscedasticity key producing precise density forecasts, our allows stochastic volatility in errors. We apply to two datasets. first application shows that BAVART yields highly competitive forecasts U.S. term structure interest rates. In second estimate using moderately sized Eurozone dataset investigate dynamic effects uncertainty economy.
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2022
ISSN: ['1941-7330', '1932-6157']
DOI: https://doi.org/10.1214/21-aoas1488