Abstract
This paper estimates aggregate measures of macroeconomic uncertainty from individual density forecasts by professional forecasters. The methodology used in the paper improves on the existing literature along two dimensions. Firstly, it controls for changes to the composition of the panel of respondents to the survey. And secondly, it assigns more weight to the information submitted by forecasters with better forecasting performance. Using data from the European Central Bank’s Survey of Professional Forecasters from 1999 Q1 to 2014 Q3, the paper finds that the effects of changes in the composition of the panel on aggregate uncertainty can be large in a statistical and economic sense. It also finds that the estimates of aggregate uncertainty that use performance-based weights differ significantly from the simple averages used in the literature and their dynamics are more consistent with the dynamics displayed by the estimates of uncertainty computed from financial indicators.
Highlights
Macroeconomic outcomes are the result of millions of decisions taken by economic agents worldwide, and the economic literature tries hard to understand the determinants of these decisions
The first is the panelcomposition problem: the existing measures of aggregate uncertainty do not take into account that the panel of professional forecasters changes from one survey to another
The second problem the existing measures of aggregate uncertainty used in the Survey of Professional Forecasters (SPF) literature suffer from is the same-weight problem: they all assign the same weight to the uncertainty perceived by each survey participant, without taking forecasting performance into consideration
Summary
Macroeconomic outcomes are the result of millions of decisions taken by economic agents worldwide, and the economic literature tries hard to understand the determinants of these decisions. They considered five different scenarios, with the minimum number of responses by panellist set at 5, 10, 15, 20 or 25 While this procedure may reduce the impact of panel changes, their measures of uncertainty still mix data from panellists with different participation patterns and, as a result, are still contaminated by variations in the panel of respondents to the survey. The second problem the existing measures of aggregate uncertainty used in the SPF literature suffer from is the same-weight problem: they all assign the same weight to the uncertainty perceived by each survey participant, without taking forecasting performance into consideration. Performance-based weights have never been used for the aggregation of the estimates of uncertainty perceived by each survey participant into an estimate of aggregate uncertainty This paper solves these two problems and is organized as follows. The paper presents the estimates of aggregate uncertainty when the individual measures of uncertainty are weighted according to each participant’s forecasting performance. The paper concludes, discusses the implications of its findings and proposes directions for future research
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