Macroeconomic Uncertainty Through the Lens of Professional Forecasters

Jo, Soojin; Sekkel, Rodrigo

doi:10.1080/07350015.2017.1356729

Cited by 72 publications

(48 citation statements)

References 67 publications

(52 reference statements)

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“…In the empirical analysis, we employ the first-releases of x t , which are most closely related to the information available to forecasters when they produce their predictions. Moreover, Jo and Sekkel (2018) show that ex-post forecast variances based on the most recent data vintage tend to be underestimated. The survey data from the SPF consist of so-called 'fixed-event' density forecasts, which are characterized by a fixed target year t and a quarterly forecast horizon h. The nature of these forecasts implies that h diminishes in each consecutive quarter in which the survey is conducted until the arrival of the realization in t. We consider both the predictions for the current and the next year.…”

Section: Datamentioning

confidence: 96%

Uncertainty Measures from Partially Rounded Probabilistic Forecast Surveys

Glas¹,

Hartmann²

2020

SSRN Journal

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Although survey-based point predictions have been found to outperform successful forecasting models, corresponding variance forecasts are frequently diagnosed as heavily distorted. Forecasters who report inconspicuously low ex-ante variances often produce squared forecast errors that are much larger on average. In this paper, we document the novel stylized fact that this variance misalignment is related to the rounding behavior of survey participants. Rounding may reflect the fact that some survey participants employ a rather judgmental approach to forecasting as opposed to using a formal model. We use the distinct numerical accuracies of panelists' reported probabilities as a means to propose several alternative and easily implementable corrections that i) can be carried out in real time, i.e., before outcomes are observed, and ii) deliver a significantly improved match between ex-ante and ex-post forecast uncertainty. According to our estimates, uncertainty about inflation, output growth and unemployment in the U.S. and the Euro area is higher after correcting for the rounding effect. The increase in the share of non-rounded responses in recent years also helps to understand the trajectory of survey-based average uncertainty during the years since the financial and sovereign debt crisis.

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Section: Datamentioning

confidence: 96%

Uncertainty Measures from Partially Rounded Probabilistic Forecast Surveys

Glas¹,

Hartmann²

2020

SSRN Journal

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“…compares the responses of GDP from the restricted model with the unrestricted model for scenarios 1 and 2, respectively 26. These …gures show qualitatively comparable contractionary e¤ects for uncertainty shocks in both models, although the di¤erences between the two scenarios are less pronounced.…”

mentioning

confidence: 87%

The nonlinear effects of uncertainty shocks

Jackson

Kliesen

Owyang

2019

Studies in Nonlinear Dynamics &Amp; Econometrics

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We consider the effects of uncertainty shocks in a nonlinear VAR that allows uncertainty to have amplification effects. When uncertainty is relatively low, fluctuations in uncertainty have small, linear effects. In periods of high uncertainty, the effect of a further increase in uncertainty is magnified. We find that uncertainty shocks in this environment have a more pronounced effect on real economic variables. We also conduct counterfactual experiments to determine the channels through which uncertainty acts. Uncertainty propagates through both the household consumption channel and through businesses delaying investment, providing substantial contributions to the decline in GDP observed after uncertainty shocks. Finally, we find evidence of the ability of systematic monetary policy to mitigate the adverse effects of uncertainty shocks.

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“…Early examples include, for example, Pitt & Shephard (1999), Aguilar & West (2000) and Chib, Nardari & Shephard (2006). More recent applications of factor models with stochastic volatility applied to macroeconomic data include Del Negro & Otrok (2008), Carriero, Clark & Marcellino (2018b), and Jo & Sekkel (2019).…”

Section: Introductionmentioning

confidence: 99%

“…Bloom (2009) uses stock market implied volatilities to proxy macroeconomic uncertainty, whereas Baker, Bloom & Davis (2016) measure uncertainty using media references to policy uncertainty. An alternative approach is to measure uncertainty as the variability common to the unforecastable component of a number of economic or financial variables, for example, Jurado, Ludvigson & Ng (2015), Scotti (2016), and Jo & Sekkel (2019).…”

Section: Introductionmentioning

confidence: 99%

Time-varying Uncertainty of the Federal Reserve’s Output Gap Estimate

Berge

2020

FEDS

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What is the output gap and when do we know it? A factor stochastic volatility model estimates the common component to forecasts of the output gap produced by the staff of the Federal Reserve, its time-varying volatility, and time-varying, horizon-specific forecast uncertainty. The common factor to these forecasts is highly procyclical and unexpected increases to the common factor are associated with persistent responses in other key macroeconomic variables. The estimates of this common factor, however, are very uncertain, even well after the fact. Output gap uncertainty increases at business cycle turning points. Lastly, increased macroeconomic uncertainty, as measured by the output gap's time-varying volatility, produces pronounced negative responses to other macroeconomic variables.

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Macroeconomic Uncertainty Through the Lens of Professional Forecasters

Cited by 72 publications

References 67 publications

Uncertainty Measures from Partially Rounded Probabilistic Forecast Surveys

Uncertainty Measures from Partially Rounded Probabilistic Forecast Surveys

The nonlinear effects of uncertainty shocks

Time-varying Uncertainty of the Federal Reserve’s Output Gap Estimate

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