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  • Krueger_Nolte2015-full

    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Banking & Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Banking & Finance, 72 2016 DOI: 10.1016/j.jbankfin.2015.05.007

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Disagreement versus uncertainty: evidence from distribution forecasts

Research output: Contribution to journalJournal article

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Disagreement versus uncertainty : evidence from distribution forecasts. / Krueger, Fabian; Nolte, Ingmar.

In: Journal of Banking and Finance, Vol. 72, No. Suppl., 11.2016, p. 172-186.

Research output: Contribution to journalJournal article

Harvard

Krueger, F & Nolte, I 2016, 'Disagreement versus uncertainty: evidence from distribution forecasts', Journal of Banking and Finance, vol. 72, no. Suppl., pp. 172-186. https://doi.org/10.1016/j.jbankfin.2015.05.007

APA

Vancouver

Author

Krueger, Fabian ; Nolte, Ingmar. / Disagreement versus uncertainty : evidence from distribution forecasts. In: Journal of Banking and Finance. 2016 ; Vol. 72, No. Suppl. pp. 172-186.

Bibtex

@article{4a2ba88fbd984d7c8ebee0c0dff59ba2,
title = "Disagreement versus uncertainty: evidence from distribution forecasts",
abstract = "We use a cross-section of economic survey forecasts to predict the distribution of US macro variables in real time. This generalizes the existing literature, which uses disagreement (i.e., the cross-sectional variance of survey forecasts) to predict uncertainty (i.e., the conditional variance of future macroeconomic quantities). Our results show that cross-sectional information can be helpful for distribution forecasting, but this information needs to be modeled in a statistically efficient way in order to avoid overfitting. A simple one-parameter model which exploits timevariation in the cross-section of survey point forecasts is found to perform well in practice.",
keywords = "Forecasting, Survey data, Density forecasting, Disagreement, Uncertainty",
author = "Fabian Krueger and Ingmar Nolte",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Journal of Banking & Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Banking & Finance, 72 2016 DOI: 10.1016/j.jbankfin.2015.05.007 ",
year = "2016",
month = nov,
doi = "10.1016/j.jbankfin.2015.05.007",
language = "English",
volume = "72",
pages = "172--186",
journal = "Journal of Banking and Finance",
issn = "0378-4266",
publisher = "Elsevier",
number = "Suppl.",

}

RIS

TY - JOUR

T1 - Disagreement versus uncertainty

T2 - evidence from distribution forecasts

AU - Krueger, Fabian

AU - Nolte, Ingmar

N1 - This is the author’s version of a work that was accepted for publication in Journal of Banking & Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Banking & Finance, 72 2016 DOI: 10.1016/j.jbankfin.2015.05.007

PY - 2016/11

Y1 - 2016/11

N2 - We use a cross-section of economic survey forecasts to predict the distribution of US macro variables in real time. This generalizes the existing literature, which uses disagreement (i.e., the cross-sectional variance of survey forecasts) to predict uncertainty (i.e., the conditional variance of future macroeconomic quantities). Our results show that cross-sectional information can be helpful for distribution forecasting, but this information needs to be modeled in a statistically efficient way in order to avoid overfitting. A simple one-parameter model which exploits timevariation in the cross-section of survey point forecasts is found to perform well in practice.

AB - We use a cross-section of economic survey forecasts to predict the distribution of US macro variables in real time. This generalizes the existing literature, which uses disagreement (i.e., the cross-sectional variance of survey forecasts) to predict uncertainty (i.e., the conditional variance of future macroeconomic quantities). Our results show that cross-sectional information can be helpful for distribution forecasting, but this information needs to be modeled in a statistically efficient way in order to avoid overfitting. A simple one-parameter model which exploits timevariation in the cross-section of survey point forecasts is found to perform well in practice.

KW - Forecasting

KW - Survey data

KW - Density forecasting

KW - Disagreement

KW - Uncertainty

U2 - 10.1016/j.jbankfin.2015.05.007

DO - 10.1016/j.jbankfin.2015.05.007

M3 - Journal article

VL - 72

SP - 172

EP - 186

JO - Journal of Banking and Finance

JF - Journal of Banking and Finance

SN - 0378-4266

IS - Suppl.

ER -