Interest Rates and the Subjective Probability Distribution of Inflation Forecasts

Lahiri, Kajal; Teigland, Christie; Zaporowski, Mark P.

doi:10.2307/1992113

“…This model highlights and extends several important results in previous work by Zarnowitz and Lambros (1987), Lahiri, Teigland, and Zaporowski (1988), Granger and Ramanathan (1984), and others.…”

Section: Which Measure Of Uncertainty?supporting

confidence: 90%

Inflation forecast uncertainty

Giordani

¹

,

Söderlind

²

2003

European Economic Review

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“…Other studies such as Lahiri, Teigland and Zaporowski (1988), Pennacchi (1991), and Neumark and Leonard (1993) quantify the effects of expected inflation and inflation uncertainty on key macroeconomic and financial variables.…”

Section: Econometric Methodologymentioning

confidence: 99%

Modeling Uncertainty: Predictive Accuracy as a Proxy for Predictive Confidence

Rich

¹

,

Tracy

²

2003

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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 AbstractThis paper evaluates current strategies for the empirical modeling of forecast behavior. In particular, we focus on the reliability of using proxies from time series models of heteroskedasticity to describe changes in predictive confidence. We address this issue by examining the relationship between ex post forecast errors and ex ante measures of forecast uncertainty from data on inflation forecasts from the Survey of Professional Forecasters. The results provide little evidence of a strong link between observed heteroskedasticity in the consensus forecast errors and forecast uncertainty. Instead, the findings indicate a significant link between observed heteroskedasticity in the consensus forecast errors and forecast dispersion. We conclude that conventional model-based measures of uncertainty may be capturing not the degree of confidence that individuals attach to their forecasts but rather the degree of disagreement across individuals in their forecasts. 1The most popular example of this modeling approach is the Autoregressive Conditional Heteroskedasticity (ARCH) model of Engle (1982) and its various extensions.It is widely recognized that macroeconomic outcomes depend critically on both peoples' expectations and the confidence attached to those expectations. Because these magnitudes are largely unobservable, a considerable amount of work has focused on the empirical modeling of forecast behavior. As a result of this effort, there is now general agreement among applied researchers on this issue. Following the tenets of the rational expectations hypothesis, subjective predictions are assumed to be optimal forecasts given all available information and are equated to the objective conditional expectation from the specific model under consideration. With regard to modeling forecast uncertainty, the prevailing approach relies on time series models of heteroskedasticity in which the variance surrounding a prediction is allowed to change over time.1 Temporal variation in subjective uncertainty is equated to the objective conditional variance of a series, with heightened (diminished) uncertainty associated with episodes of decreased (increased) predictability.In spite of the important role played by uncertainty in economic behavior, there is a very limited understanding about the nature of this process. Consequently, the use of time series models of heteroskedasticity to generate estimates of uncertainty has la...

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“…The first term on the right-hand side of (1) Lahiri, Tiegland and Zaporowski (1988) calculate the first four moments of the individual US-SPF, and then use time series of their average values in their study. In the course of their analysis, they obtain a version of the decomposition in equation (1).…”

Section: A Variable Definitionsmentioning

confidence: 99%

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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Interest Rates and the Subjective Probability Distribution of Inflation Forecasts

Cited by 97 publications

References 21 publications

Inflation forecast uncertainty

Inflation forecast uncertainty

Modeling Uncertainty: Predictive Accuracy as a Proxy for Predictive Confidence

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

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