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Constructing densit...
Constructing density forecasts from quantile regressions : Multimodality in macrofinancial dynamics
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- Mitchell, James (author)
- Federal Reserve Bank of Cleveland, Cleveland Ohio, USA
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- Poon, Aubrey, 1987- (author)
- Örebro universitet,Handelshögskolan vid Örebro Universitet,University of Kent, Canterbury, UK
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- Zhu, Dan (author)
- Monash University, Clayton, Australia
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(creator_code:org_t)
- 2024
- 2024
- English.
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In: Journal of applied econometrics (Chichester, England). - : John Wiley & Sons. - 0883-7252 .- 1099-1255.
- Related links:
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https://doi.org/10.1...
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Abstract
Subject headings
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- Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the “data speak.” Simulation evidence and an application revisiting GDP growth uncertainties in the United States demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regressions. They identify its ability to unmask deviations from symmetrical and unimodal densities. The dominant macroeconomic narrative becomes one of the evolution, over the business cycle, of multimodalities rather than asymmetries in the predictive distribution of GDP growth when conditioned on financial conditions.
Subject headings
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Nationalekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Economics (hsv//eng)
Keyword
- density forecasts
- financial conditions
- quantile regressions
Publication and Content Type
- ref (subject category)
- art (subject category)
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