What Do We Lose When We Average Expectations?
No 2016-013, Working Papers from The George Washington University, Department of Economics, Research Program on Forecasting
In this paper, I use the Bloomberg Survey of forecasts to assess if evaluating the distribution of expectations will lead to important additional insights over the evaluation of the simple average. I first introduce new approaches that allow me to assess the forecast accuracy and the information rigidity at the individual level despite a large share of missing data. Applying these new approaches, I find that taking into account the distribution can significantly improve the predictive power of the survey. For example, I find that the part of uncertainty measured by disagreement can improve the prediction of recessions in a dynamic probit model relative to the simple average. On information rigidity, I find that some of the rigidity found at the aggregate level likely stems from the aggregation process. Together, my findings suggest that we should look at individual expectations whenever possible as important insights are lost by just looking at aggregate expectations.
Keywords: Expectations; Bloomberg Survey; Forecast Evaluation; Uncertainty; Dynamic Probit (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 E17 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://www2.gwu.edu/~forcpgm/2016-013.pdf First version, 2016 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gwc:wpaper:2016-013
Access Statistics for this paper
More papers in Working Papers from The George Washington University, Department of Economics, Research Program on Forecasting Contact information at EDIRC.
Bibliographic data for series maintained by Tara M. Sinclair ().