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researcher

Florian Huber

39 papers hereh-index 241.9k citations115 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author13
  • middle author22
  • last author4

Across the 39 of 39 papers where every author was matched, so the position is known.

fields
  • econ.EM31
  • math.PR3
  • econ.GN2
  • stat.ME2
  • stat.CO1
same name
  • Florian Huber — 6 papers, h 3
  • Florian Huber — 5 papers, h 2
  • Florian Huber — 4 papers, h 10
  • Florian Huber — 4 papers, h 4
  • Florian Huber — 3 papers, h 2
  • Florian Huber — 3 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162023
most citedSparse Bayesian vector autoregressions in huge dimensions

86 citations · 156 across the 36 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

econ.EM2023

Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks

Florian Huber, Gary Koop

The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration moti…

econ.EM2023

Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification

Florian Huber, Massimiliano Marcellino, Tobias Scheckel

Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or respon…

econ.GN2023

A tale of two tails: 130 years of growth-at-risk

Martin Gächter, Elias Hasler, Florian Huber

We extend the existing growth-at-risk (GaR) literature by examining a long time period of 130 years in a time-varying parameter regression model. We identify several important insi…

econ.EM2023★ 1 cited

Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions

Jan Prüser, Florian Huber

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 p…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.