Working Paper: CEPR ID: DP17461
Authors: Todd Clark; Florian Huber; Gary Koop; Massimiliano Marcellino; Michael Pfarrhofer
Abstract: We develop multivariate time series models using Bayesian additive regression trees that posit nonlinearities among macroeconomic variables, their lags, and possibly their lagged errors. The error variances can be stable, feature stochastic volatility, or follow a nonparametric specification. We evaluate density and tail forecast performance for a set of US macroeconomic and financial indicators. Our results suggest that the proposed models improve forecast accuracy both overall and in the tails. Another finding is that when allowing for nonlinearities in the conditional mean, heteroskedasticity becomes less important. A scenario analysis reveals nonlinear relations between predictive distributions and financial conditions.
Keywords: nonparametric; VAR; regression trees; macroeconomic forecasting; scenario analysis
JEL Codes: C11; C32; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| BART models (C59) | forecast accuracy (C53) |
| BART models (C59) | tail forecasting performance (C53) |
| allowing for nonlinearities in the conditional mean (C51) | importance of heteroskedasticity (C21) |
| BART models (C59) | essential dynamics (D51) |
| BART models (C59) | forecasting accuracy in tail distributions (C53) |
| financial conditions (E66) | predictive distributions (C46) |