Todd Clark, Florian Huber, Gary Koop
arXiv 19 Aug 2025 · Econometrics
arXiv:2508.13972 · PDF · Extracted main text
This paper proposes a Vector Autoregression augmented with nonlinear factors that are modeled nonparametrically using regression trees. There are four main advantages of our model. First, modeling potential nonlinearities nonparametrically lessens the risk of mis-specification. Second, the use of factor methods ensures that departures from linearity are modeled parsimoniously. In particular, they exhibit functional pooling where a small number of nonlinear factors are used to model common nonlinearities across variables. Third, Bayesian computation using MCMC is straightforward even in very high dimensional models, allowing for efficient, equation by equation estimation, thus avoiding computational bottlenecks that arise in popular alternatives such as the time varying parameter VAR. Fourth, existing methods for identifying structural economic shocks in linear factor models can be adapted for the nonlinear case in a straightforward fashion using our model. Exercises involving artificial and macroeconomic data illustrate the properties of our model and its usefulness for forecasting and structural economic analysis.
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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, George, and McCulloch (2010) BART: Bayesian additive regression trees | 0.950 | 7 | 3 | 86% |
| 2 | Korobilis (2022) A new algorithm for structural restrictions in Bayesian vector autoregressions | 0.928 | 4 | 3 | 100% |
| 3 | McCracken and Ng (2021) FRED-QD: A Quarterly Database for Macroeconomic Research | 0.737 | 3 | 2 | 100% |
| 4 | Baumeister and Hamilton (2015) Sign restrictions, structural vector autoregressions, and useful prior information | 0.644 | 2 | 2 | 100% |
| 5 | Barnichon and Matthes (2018) Functional approximation of impulse responses | 0.585 | 3 | 1 | 100% |
| 6 | Velasco (2025) Let the Tree Decide: FABART A Non-Parametric Factor Model | 0.585 | 3 | 1 | 100% |
| 7 | Makalic and Schmidt (2015) A simple sampler for the horseshoe estimator | 0.511 | 2 | 2 | 50% |
| 8 | Angrist, Jorda, and Kuersteiner (2018) Semiparametric estimates of monetary policy effects: String theory revisited | 0.511 | 2 | 1 | 100% |
| 9 | Baumeister, Frank, Huber, and Koop (2025) Oil, inflation expectations, and household characteristics: A nonlinear heterogeneous agent var approach | 0.405 | 1 | 1 | 100% |
| 10 | Carriero, Clark, and Marcellino (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 39 scored citations.