The Relationships among Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts

Rich, Robert W.; Tracy, Joseph

doi:10.1162/rest.2009.11167

“…Interestingly, the observed low correlations of disagreement and uncertainty are comparable to those reported in Rich and Tracy (2010) for the US-SPF. Taken together, the evidence from the ECB-SPF provides little support for the use of disagreement as a proxy for uncertainty.…”

Section: B Estimated Relationships Between Uncertainty and Disagreementsupporting

confidence: 84%

“…Moreover, we find little support for the claim that the nature of the comovement between disagreement and uncertainty depends on the extent of volatility in the sample period. 18 These results, along with those of Rich and Tracy (2010) for the US-SPF and Boero, Smith and Wallis (2008) for the BOE-SEF, offer consistent evidence across three surveys that the distinction between disagreement and uncertainty as concepts extends as well to their empirical counterparts. Further, the evidence from these three studies drawn from three different 17 Several studies [Vroman (1989), Emery (1993) and Davis and Kanago (1997)] have argued that a measure of relative uncertainty is more appropriate.…”

Section: B Estimated Relationships Between Uncertainty and Disagreementmentioning

confidence: 69%

“…The results of Giordani and Soderlind indicate a positive association between disagreement and uncertainty that is both economically and statistically significant. Rich and Tracy (2010) argue that Giordani and Soderlind's conclusion is problematic due to the poor fit of the normal approximation to many of the individual histograms, as well as to their use of a measure of disagreement derived from the density forecast data rather than from the point forecast data. 15 Examining matched point and density forecasts, as well as deriving uncertainty measures based on the Wallis decomposition 15 Boero, Smith and Wallis (2012) also discuss the problematic nature of fitting normal distributions to two-bin histograms.…”

Section: Survey Of Professional Forecasters (Us-spf) For Their Analysmentioning

confidence: 96%

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The Measurement and Behavior of Uncertainty: Evidence from the ECB Survey of Professional Forecasters

Abel

¹

,

Rich

²

,

Song³

et al. 2015

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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. Terms of use: Documents in AbstractWe use matched point and density forecasts of output growth and infl ation from the ECB Survey of Professional Forecasters to derive measures of forecast uncertainty, forecast dispersion, and forecast accuracy. We construct uncertainty measures from aggregate density functions as well as from individual histograms. The uncertainty measures display countercyclical behavior, and there is evidence of increased uncertainty for output growth and infl ation since 2007. The results also indicate that uncertainty displays a very weak relationship with forecast dispersion, corroborating the fi ndings of other recent studies indicating that disagreement is not a valid proxy for uncertainty. In addition, we fi nd no correspondence between movements in uncertainty and predictive accuracy, suggesting that time-varying conditional variance estimates may not provide a reliable proxy for uncertainty. Last, using a regression equation that can be interpreted as a (G)ARCH-M-type model, we fi nd limited evidence of linkages between uncertainty and levels of infl ation and output growth.

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“…He measures disagreement as the cross-sectional standard deviation of individual inflation forecasts and argues that this variable captures inflation uncertainty. Using data on individual point as well as density forecasts from the US Survey of Professional Forecasters, Rich and Tracy (2010) show that disagreement about US inflation is not systematically related to measures of inflation uncertainty. Boero, Smith, and Wallis (2008) study the relationship between forecast uncertainty and disagreement up to two years into the future for a UK survey of professional forecasts and find a sustained reduction of inflation uncertainty after the introduction of a formal inflation targeting regime by the Bank of England.…”

Section: Three Novel Facts About Forecaster Disagreementmentioning

confidence: 94%

“…Dovern, Fritsche, and Slacalek (2012) rely on the Consensus Economics survey to document that the properties of near-term disagreement about real output growth, inflation, and shortterm interest rates differ across variables and across G7 countries. A number of papers discuss the relationship between disagreement about US inflation and measures of inflation uncertainty as implied by density forecasts, see for example Rich and Tracy (2010). Lahiri and Sheng (2008) and Patton and Timmermann (2010) study disagreement for fixed-target forecasts up to two years ahead also using the Consensus Economics survey.…”

Section: Introductionmentioning

confidence: 99%

Noisy Information and Fundamental Disagreement

Eusepi¹,

Crump²,

Moench³

et al. 2013

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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. Terms of use: Documents in AbstractWe study the term structure of disagreement of professional forecasters for key macroeconomic variables. We document a novel set of facts: 1) forecasters disagree at all horizons, including the very long run; 2) the shape of the term structure of disagreement differs markedly across variables: the term structure is downward-sloping for real output growth, relatively flat for CPI inflation, and upward-sloping for the federal funds rate; 3) disagreement is time varying at all horizons, including the very long run. We suggest a model with noisy information and shifting long-run beliefs that is consistent with these stylized facts. Notably, our model does not rely on the heterogeneity of prior beliefs, bounded rationality, or differences in the precision of signals across agents.

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How Informative are the Subjective Density Forecasts of Macroeconomists?

Kenny

¹

,

Kostka

²

,

Masera

³

2014

Journal of Forecasting

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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. Terms of use: Documents in EconStor may ABSTRACTIn this paper, we propose a framework to evaluate the subjective density forecasts of macroeconomists using micro data from the euro area Survey of Professional Forecasters (SPF). A key aspect of our analysis is the evaluation of the entire predictive densities, including an evaluation of the impact of density features such as location, spread, skew and tail risk on density forecast performance. Overall, we find considerable heterogeneity in the performance of the surveyed densities at the individual level. Relative to a set of simple benchmarks, this performance is somewhat better for GDP growth than for inflation, although in the former case it diminishes substantially with the forecast horizon. In addition, we report evidence of some improvement in the relative performance of expert densities during the recent period of macroeconomic volatility. However, our analysis also reveals clear evidence of overconfidence or neglected risks in expert probability assessments, as reflected in frequent occurrences of events which are assigned a zero probability. Moreover, higher moment features of expert densities, such as skew or the degree of probability mass in their tails, are shown not to contribute significantly to improvements in individual density forecast performance.Keywords: forecast evaluation, neglected risks, real-time data, Survey of Professional Forecasters Non-technical summaryEconomic agents and policy makers often rely on macroeconomic risk and uncertainty assessments to inform their choices and decisions. Formally, such expert assessments are represented in the form of a complete density forecast which summarises the probability attaching to all possible future outcomes and not just a central or most likely outcome. Not much, however, is really known concerning the overall quality and accuracy of this type of information. For example, are macroeconomic experts able to provide risk and uncertainty predictions that are superior to simple rules of thumb or even very naïve statistical statements, e.g. equivalent to flipping a coin? If so, which specific features of these density forecasts contribute to strengthening their predictive power? Additionally, are macroeconomic experts better able to assess the uncertainty surrounding some economic variables, such as output growth, rather than others such as inflation? Or, do such risk assessments have equivalent "validity" or information content at both sho...

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The Relationships among Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts

Cited by 132 publications

References 8 publications

The Measurement and Behavior of Uncertainty: Evidence from the ECB Survey of Professional Forecasters

The Measurement and Behavior of Uncertainty: Evidence from the ECB Survey of Professional Forecasters

Noisy Information and Fundamental Disagreement

How Informative are the Subjective Density Forecasts of Macroeconomists?

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