arXiv 31 Jan 2023 · Econometrics · publishedJournal of Applied Econometrics (2023) · 9 citations (OpenAlex)
arXiv:2301.13604 · PDF · DOI · OpenAlex · Extracted main text
Modeling and predicting extreme movements in GDP is notoriously difficult and the selection of appropriate covariates and/or possible forms of nonlinearities are key in obtaining precise forecasts. In this paper, our focus is on using large datasets in quantile regression models to forecast the conditional distribution of US GDP growth. To capture possible non-linearities, we include several nonlinear specifications. The resulting models will be huge dimensional and we thus rely on a set of shrinkage priors. Since Markov Chain Monte Carlo estimation becomes slow in these dimensions, we rely on fast variational Bayes approximations to the posterior distribution of the coefficients and the latent states. We find that our proposed set of models produces precise forecasts. These gains are especially pronounced in the tails. Using Gaussian processes to approximate the nonlinear component of the model further improves the good performance, in particular in the right tail.
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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 | Adrian, T., Boyarchenko, N., and Giannone, D (2019) Vulnerable growth | 0.928 | 4 | 4 | 100% |
| 2 | Carriero, A., Clark, T. E., and Marcellino, M. G (2022) Specification choices in quantile regression for empirical macroeconomics | 0.874 | 5 | 2 | 100% |
| 3 | Mitchell, J., Poon, A., and Mazzi, G. L (2022) Nowcasting euro area gdp growth using bayesian quantile regression | 0.811 | 4 | 2 | 100% |
| 4 | Griffin, J. E. and Brown, P. J (2010) Inference with normal-gamma prior distributions in regression problems | 0.737 | 3 | 2 | 100% |
| 5 | Clark, T. E., Huber, F., Koop, G., Marcellino, M., and Pfarrhofer, M (2022) Tail forecasting with multivariate bayesian additive regression trees self | 0.737 | 3 | 2 | 100% |
| 6 | Kohns, D. and Szendrei, T (2021) Decoupling shrinkage and selection for the bayesian quantile regression | 0.737 | 3 | 2 | 100% |
| 7 | Diebold, F. X. and Mariano, R (1995) Comparing predictive accuracy | 0.693 | 5 | 1 | 100% |
| 8 | Harvey, D., Leybourne, S., and Newbold, P (1997) Testing the equality of prediction mean squared errors | 0.644 | 4 | 1 | 100% |
| 9 | Blei, D. M., Kucukelbir, A., and McAuliffe, J. D (2017) Variational inference: A review for statisticians | 0.644 | 2 | 2 | 100% |
| 10 | McCracken, M. W. and Ng, S (2016) Fred-md: A monthly database for macroeconomic research | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 64 scored citations.