Todd E. Clark, Florian Huber, Gary Koop, Massimiliano Marcellino, Michael Pfarrhofer
arXiv 7 Oct 2021 · Econometrics
arXiv:2110.03411 · PDF · Extracted main text
We develop a Bayesian non-parametric quantile panel regression model. Within each quantile, the response function is a convex combination of a linear model and a non-linear function, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information at the pth quantile is captured through a conditionally heteroscedastic latent factor. The non-parametric feature of our model enhances flexibility, while the panel feature, by exploiting cross-country information, increases the number of observations in the tails. We develop Bayesian Markov chain Monte Carlo (MCMC) methods for estimation and forecasting with our quantile factor BART model (QF-BART), and apply them to study growth at risk dynamics in a panel of 11 advanced economies.
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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 | Huber, Koop, Onorante, Pfarrhofer, and Schreiner (2020) Nowcasting in a pandemic using non-parametric mixed frequency VARs | 0.737 | 3 | 2 | 100% |
| 2 | Chipman, George, and McCulloch (2010) BART: Bayesian additive regression trees | 0.693 | 5 | 1 | 100% |
| 3 | Adrian, Boyarchenko, and Giannone (2019) Vulnerable growth | 0.644 | 2 | 2 | 100% |
| 4 | Stock and Watson (2005) Understanding changes in international business cycle dynamics | 0.511 | 2 | 1 | 100% |
| 5 | Adrian, Grinberg, Liang, and Malik (2018) The term structure of growth-at-risk | 0.511 | 2 | 1 | 100% |
| 6 | Korobilis, Landau, Musso, and Phella (2021) The time-varying evolution of inflation risks | 0.511 | 2 | 1 | 100% |
| 7 | Kozumi and Kobayashi (2011) Gibbs sampling methods for bayesian quantile regression | 0.511 | 2 | 1 | 100% |
| 8 | Pfarrhofer (2021) Tail forecasts of inflation using time-varying parameter quantile regressions self | 0.511 | 2 | 1 | 100% |
| 9 | Bai, Carriero, Clark, and Marcellino (2020) Macroeconomic forecasting in a multi-country context | 0.405 | 1 | 1 | 100% |
| 10 | Ferrara, Mogliani, and Sahuc (2019) Real-time high frequency monitoring of growth-at-risk | 0.405 | 1 | 1 | 100% |
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