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Measuring Output Gap Nowcast Uncertainty

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  • Anthony Garratt
  • James Mitchell
  • Shaun P. Vahey

Abstract

We propose a methodology to gauge the uncertainty in output gap nowcasts across a large number of commonly-deployed vector autoregressions in US inflation and various measures of the output gap. Our approach constructs ensemble nowcast densities using a linear opinion pool. This yields well-calibrated nowcasts for US inflation in real time from 1991q2 to 2010q1, in contrast to those from a univariate autoregressive benchmark. The ensemble nowcast densities for the output gap are considerably more complex than for a single VAR specification. They cannot be described adequately by the first two moments of the forecast densities. To illustrate the usefulness of our approach, we calculate the probability of a negative output gap at around 45 percent between 2004 and 2007. Despite the Greenspan policy regime, and some large point estimates of the output gap, there remained a substantial risk that output was below potential in real time. Our ensemble approach also facilitates probabilistic assessments of “alternative scenarios”. A “dove” scenario (based on distinct output gap measurements) typically raises substantially the probability of a negative output gap (including 2004 through 2007) but has little impact in slumps, in our illustrative example.

Suggested Citation

  • Anthony Garratt & James Mitchell & Shaun P. Vahey, 2011. "Measuring Output Gap Nowcast Uncertainty," CAMA Working Papers 2011-16, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2011-16
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    1. repec:eee:ecmode:v:64:y:2017:i:c:p:153-171 is not listed on IDEAS
    2. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    3. Wolden Bache, Ida & Sofie Jore, Anne & Mitchell, James & Vahey, Shaun P., 2011. "Combining VAR and DSGE forecast densities," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1659-1670, October.
    4. Tommaso Proietti & Martyna Marczak & Gianluigi Mazzi, 2017. "Euromind‐ D : A Density Estimate of Monthly Gross Domestic Product for the Euro Area," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 683-703, April.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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