arXiv 4 Dec 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2312.01881 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a new Bayesian machine learning model that can be applied to large datasets arising in macroeconomics. Our framework sums over many simple two-component location mixtures. The transition between components is determined by a logistic function that depends on a single threshold variable and two hyperparameters. Each of these individual models only accounts for a minor portion of the variation in the endogenous variables. But many of them are capable of capturing arbitrary nonlinear conditional mean relations. Conjugate priors enable fast and efficient inference. In simulations, we show that our approach produces accurate point and density forecasts. In a real-data exercise, we forecast US macroeconomic aggregates and consider the nonlinear effects of financial shocks in a large-scale nonlinear VAR.
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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 | Clark, Todd E, Florian Huber, Gary Koop, Massimiliano Marcellino, an… (2023) Tail forecasting with multivariate bayesian additive regression trees self | 1.000 | 6 | 3 | 100% |
| 2 | Chipman, Hugh A., Edward I. George, and Robert E. McCulloch (2010) BART: Bayesian additive regression trees | 1.000 | 5 | 3 | 100% |
| 3 | Gilchrist, Simon and Egon Zakrajsek (2012) Credit spreads and business cycle fluctuations | 0.928 | 5 | 3 | 80% |
| 4 | Lubrano, Michel (2001) Smooth transition garch models: A bayesian perspective | 0.644 | 2 | 2 | 100% |
| 5 | McCracken, Michael and Serena Ng (2020) Fred-qd: A quarterly database for macroeconomic research | 0.511 | 2 | 2 | 50% |
| 6 | Goulet Coulombe, Philippe (2020) The macroeconomy as a random forest | 0.511 | 2 | 1 | 100% |
| 7 | Barnichon, Regis, Christian Matthes, and Alexander Ziegenbein (2022) Are the effects of financial market disruptions big or small? | 0.511 | 2 | 1 | 100% |
| 8 | Cybenko, George (1989) Approximation by superpositions of a sigmoidal function | 0.511 | 2 | 1 | 100% |
| 9 | Huber, Florian, Gary Koop, Luca Onorante, Michael Pfarrhofer, and Jo… (2023) Nowcasting in a pandemic using non-parametric mixed frequency vars self | 0.511 | 2 | 1 | 100% |
| 10 | Mumtaz, Haroon and Michele Piffer (2022) Impulse response estimation via flexible local projections | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 42 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Asymmetries in Financial Spillovers | 0.811 | 4 | 2 |