Measuring forecast uncertainty by disagreement: The missing link

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Using a standard decomposition of forecasts errors into common and idiosyncratic shocks, we show that aggregate forecast uncertainty can be expressed as the disagreement among the forecasters plus the perceived variability of future aggregate shocks. Thus, the reliability of disagreement as a proxy for uncertainty will be determined by the stability of the forecasting environment, and the length of the forecast horizon. Using density forecasts from the Survey of Professional Forecasters, we find direct evidence in support of our hypothesis. Our results support the use of GARCH-type models, rather than the ex post squared error in consensus forecasts, to estimate the ex ante variability of aggregate shocks as a component of aggregate uncertainty. Terms of use: Documents in EconStor mayJEL Code: E17, E37.

Inflation uncertainty revisited: a proposal for robust measurement

“…Hence, one explanation for the decline of the idiosyncratic part of survey disagreement is that forecasters tend to cluster their forecasts around the consensus during turbulent times. Note that our results are also consistent with the theoretical considerations by Lahiri and Sheng (2010b) who assume that individual forecast errors are driven by common and idiosyncratic shocks. Under these assumptions, they show that disagreement is a reliable proxy for overall uncertainty only during stable periods, that is, whenever the shocks common to all forecasters are small.…”

“…As SPF forecasts are usually published at the end of the first quarter, we compare the value of SPF uncertainty with the value of PC1 in March of a respective year. To calculate forecaster-specific uncertainty σ i , we follow D'Amico and Orphanides (2008), and Lahiri and Sheng (2010b) and use a non-parametric procedure. We obtain SPF uncertainty as the average of individual standard deviations adding a Sheppard correction.…”

Understanding the Sources of Macroeconomic Uncertainty

“…In this respect our contribution is similar to that of Lahiri and Sheng (2010), who consider the relationship between aggregate uncertainty and disagreement over the business cycle, yet measure it in terms of uncertainty and disagreement about the mean of the distribution, as opposed to the whole distribution. Our approach further enables us to distinguish between measures of realized volatility, ex-ante uncertainty and bias.…”

“…For example, Patton and Timmermann (2010) study disagreement among professional forecasters, but do not relate disagreement to measures of uncertainty, while Lahiri and Sheng (2010) consider the relationship of aggregate uncertainty and disagreement over the business cycle, yet they do not distinguish between risk and uncertainty. Jurado, Ludvigson and Ng (2015) use the forecast error variance as a measure of uncertainty, while D'Amico and Orphanides (2014) consider ex-ante measures of risk for in ‡ation forecasting.…”

Time-varying combinations of predictive densities using nonlinear filtering

“…Left and right charts in Figure 15 look similar, but the nonparametric estimated forecasts span wider intervals as further uncertainties are considered in their construction. Both parametric and nonparametric estimates tend to understate the predictive uncertainty as reported in Boero et al [2008] and Lahiri and Sheng [2010].…”