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

Rich, Robert W.; Song, Jing-Sheng; Tracy, Joseph

doi:10.2139/ssrn.2192510

Cited by 23 publications

(25 citation statements)

References 46 publications

(11 reference statements)

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“…20 This is also documented in Rich, Song, and Tracy (2012), who find that uncertainty measures stemming from the ECB-SPF display countercyclical behavior and find evidence of increased inflation uncertainty since 2007.…”

Section: Estimated Modelmentioning

confidence: 82%

“…7 Thus, denoting by σ 2 agg,th the conditional variance of the aggregate distribution, the proxy for 5 Such uncertainty measures based on the diffuseness of probability distributions have been considered by Zarnowitz and Lambros (1987), Conflitti (2011), Rich, Song, andTracy (2012), Boero, Smith, and Wallis (2014), and D'Amico and Orphanides (2014), among others.…”

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confidence: 99%

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Measuring Inflation Anchoring and Uncertainty: A US and Euro Area Comparison

Grishchenko

Mouabbi²,

Renne³

2017

FEDS

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We use several US and euro-area surveys of professional forecasters to estimate a dynamic factor model of inflation featuring time-varying uncertainty. We obtain survey-consistent distributions of future inflation at any horizon, both in the US and the euro area. Equipped with this model, we propose a novel measure of the anchoring of inflation expectations that accounts for inflation uncertainty. Our results suggest that following the Great Recession, inflation anchoring improved in the US, while mild de-anchoring occurred in the euro area. As of our sample end, both areas appear to be equally anchored.JEL codes: C32, E31, E44

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Section: Estimated Modelmentioning

confidence: 82%

mentioning

confidence: 99%

Measuring Inflation Anchoring and Uncertainty: A US and Euro Area Comparison

Grishchenko

Mouabbi²,

Renne³

2017

FEDS

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“…Each forecaster could be extremely certain, but there could still be a high degree of disagreement (and vice versa). Some authors have argued that, in light of this distinction, forecast dispersion is a poor proxy for uncertainty (Rich et al ., ).…”

Section: Measuring Economic Uncertainty In Australiamentioning

confidence: 97%

Measuring Economic Uncertainty and Its Effects

Moore

2017

Economic Record

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I construct a monthly index of economic uncertainty for Australia. Economic uncertainty rose to historically high levels during the financial crisis and remained elevated until late 2013. The index is: higher around recessions, elections, monetary policy surprises and some major events; tends to increase faster than it decreases; and is driven by both domestic and foreign factors. I use the index to assess how uncertainty affects the Australian economy. Consistent with the ‘real options’ channel of uncertainty, I find that it reduces investment and employment growth. Similarly, uncertainty raises the household saving ratio, consistent with the ‘precautionary savings’ channel.

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“…Diebold, Gunther and Tay (1998) Rich and Tracy (2010), Conflitti (2011) and Rich, Song and Tracey (2012).…”

Section: In This Paper We Exploit the Microeconomic Information Contamentioning

confidence: 99%

“…Therefore it is of interest to also check the robustness with respect to alternative measures of the dispersion in the expert densities. Rich, Song and Tracey (2012) have suggested the Inter Quartile Range (IQR) as providing a robust measure of individual uncertainty as it will tend to be less influenced by outliers and also to other sources of measurement error. Hence, we have therefore recomputed all our regressions using this measure and the results are reported in Table 3b).…”

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confidence: 99%

Density characteristics and density forecast performance: a panel analysis

2014

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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. 1 Terms of use: Documents in EconStor may NON-TECHNICAL SUMMARYIn this paper we exploit the microeconomic information contained in the European Central Bank (ECB) Survey of Professional Forecasters (SPF) as a means to contribute further to understanding the predictive performance of surveyed density forecasts. In particular, we examine the possible role of density features such as their location, spread, skewness and tail risk in determining density forecast performance both over time and across individuals. Understanding this link is of relevance to both forecast users -such as the ECB -and forecast producers. In particular, the insights from our study are of interest to survey users who may rely on specific density features when evaluating different policy choices (e.g. relating to tail risks or possible skewness in the distributions).Moreover, density forecast producers, including those forecast producers responding to the SPF questionnaire, can potentially improve their density forecast performance by gaining an understanding about how their density features have impacted on their historical density performance.Our analysis focuses on the one and two-year horizon density forecasts for euro area real output growth and consumer price inflation. We begin by constructing individual measures of density forecast performance from this dataset. Our preferred performance measure is the Ranked Probability Score (RPS) which is based on the entire predictive distribution and rewards forecasters who concentrate a high probability mass in regions of their density that are close to where the outcome occurs. Next, we estimate directly at the individual level key characteristics of the SPF densities such as their means and higher moment features such as their variances, their skewness and tail probability mass. We then proposes a set of cross-sectional and fixed effect panel regressions to examine the role of key distributional features in explaining density forecast performance both across time and across individuals. Controlling for the role of differences in point forecast accuracy (density location) as well as for other common shocks impacting on aggregate density performance, these regressions help shed light on whether or not higher order density characteristics, such as variance, skewness or the fatness of a density forecast's tails can contribute to improving forecast performance. Such a mode of analysis, responds to a clear need to generate empirical evidence...

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

Cited by 23 publications

References 46 publications

Measuring Inflation Anchoring and Uncertainty: A US and Euro Area Comparison

Measuring Inflation Anchoring and Uncertainty: A US and Euro Area Comparison

Measuring Economic Uncertainty and Its Effects

Density characteristics and density forecast performance: a panel analysis

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