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
Accepted author manuscript, 2.24 MB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -