Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions

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dc.contributor.authorPrüser, Jan
dc.contributor.authorHuber, Florian
dc.date.accessioned2025-04-03T08:21:51Z
dc.date.available2025-04-03T08:21:51Z
dc.date.issued2023-12-26
dc.description.abstractModeling 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 nonlinearities, 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.en
dc.identifier.urihttp://hdl.handle.net/2003/43591
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25424
dc.language.isoen
dc.relation.ispartofseriesJournal of applied econometrics; 39(2)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectglobal–local priorsen
dc.subjectgrowth at risken
dc.subjectlarge datasetsen
dc.subjectnonlinear modelsen
dc.subjectquantile regressionen
dc.subject.ddc310
dc.titleNonlinearities in macroeconomic tail risk through the lens of big data quantile regressionsen
dc.typeText
dc.type.publicationtypeResearchArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationPrüser, J., & Huber, F. (2024). Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions. Journal of Applied Econometrics, 39(2), 269–291. https://doi.org/10.1002/jae.3018
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/jae.3018

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