arXiv 29 Jun 2020 · Econometrics · publishedThe Annals of Applied Statistics (2022) · 36 citations (OpenAlex)
arXiv:2006.16333 · PDF · DOI · OpenAlex · Extracted main text
Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This assumption might be overly restrictive and could have a deleterious impact on forecasting accuracy. As a solution, we propose combining VAR with Bayesian additive regression tree (BART) models. The resulting Bayesian additive vector autoregressive tree (BAVART) model is capable of capturing arbitrary non-linear relations between the endogenous variables and the covariates without much input from the researcher. Since controlling for heteroscedasticity is key for producing precise density forecasts, our model allows for stochastic volatility in the errors. We apply our model to two datasets. The first application shows that the BAVART model yields highly competitive forecasts of the US term structure of interest rates. In a second application, we estimate our model using a moderately sized Eurozone dataset to investigate the dynamic effects of uncertainty on the economy.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chipman, H. A., George, E. I., and McCulloch, R. E (2010) BART: Bayesian additive regression trees | 0.920 | 9 | 4 | 78% |
| 2 | Caggiano, G., Castelnuovo, E., and Pellegrino, G (2017) Estimating the real effects of uncertainty shocks at the Zero Lower Bound | 0.737 | 3 | 2 | 100% |
| 3 | Caggiano, G., Castelnuovo, E., and Nodari, G (2021) Uncertainty and monetary policy in good and bad times | 0.737 | 3 | 2 | 100% |
| 4 | Diebold, F. X. and Li, C (2006) Forecasting the term structure of government bond yields | 0.737 | 3 | 2 | 100% |
| 5 | Mumtaz, H. and Theodoridis, K (2018) The changing transmission of uncertainty shocks in the US | 0.737 | 3 | 2 | 100% |
| 6 | Bloom, N (2009) The impact of uncertainty shocks | 0.644 | 4 | 1 | 100% |
| 7 | Jurado, K., Ludvigson, S. C., and Ng, S (2015) Measuring uncertainty | 0.644 | 4 | 1 | 100% |
| 8 | Alessandri, P. and Mumtaz, H (2019) Financial regimes and uncertainty shocks | 0.644 | 2 | 2 | 100% |
| 9 | Carriero, A., Kapetanios, G., and Marcellino, M (2012) Forecasting government bond yields with large Bayesian vector autoregressions | 0.644 | 2 | 2 | 100% |
| 10 | Crespo Cuaresma, J. C., Huber, F., and Onorante, L (2020) Fragility and the effect of international uncertainty shocks self | 0.644 | 2 | 2 | 100% |
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