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Comparing Measures of Potential Output

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  • Amy Y. Guisinger
  • Michael T. Owyang
  • Hannah Shell

Abstract

One of the goals of stabilization policy is to reduce the output gap?the difference between potential and actual output?during downturns. Potential output, however, is an unobserved variable whose definition can vary. For example, some view potential output as the level of output that can be produced when employment is at the natural rate. Others use trend measures of output to measure potential. We survey some of these measures using both full-sample data (all of the data that would be available through June 2017) and real-time data (the actual data that would have been available at different points in the sample). We construct six different measures of potential: a linear trend, a quadratic trend, the Congressional Budget Office measure, and three filtered trends. We compare these measures across methods and across time. We also use the measures to compute the monetary policy prescription in a standard interest rate rule and find very little difference across methods.

Suggested Citation

  • Amy Y. Guisinger & Michael T. Owyang & Hannah Shell, 2018. "Comparing Measures of Potential Output," Review, Federal Reserve Bank of St. Louis, vol. 100(4), pages 297-316.
    • Handle: RePEc:fip:fedlrv:00107
    DOI: doi.org/10.20955/r.100.297-316
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    References listed on IDEAS

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    Cited by:

    1. Frédérique Bec & Patrick Kanda, 2019. "Is inflation driven by survey-based, VAR-based or myopic expectations?," Working Papers hal-02175836, HAL.
    2. Bec, Frédérique & Kanda, Patrick, 2020. "Is inflation driven by survey-based, VAR-based or myopic expectations? An empirical assessment from US real-time data," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    3. Dovern, Jonas & Zuber, Christopher, 2020. "How economic crises damage potential output – Evidence from the Great Recession," Journal of Macroeconomics, Elsevier, vol. 65(C).
    4. Granados, Camilo & Parra-Amado, Daniel, 2024. "Estimating the output gap after COVID: How to address unprecedented macroeconomic variations," Economic Modelling, Elsevier, vol. 135(C).
    5. Cicilia Anggadewi Harun & Wishnu Mahraddika & Jati Waluyo & Pakasa Bary & Rieska Indah Astuti & Fauzan Rachman & Rizky Primayudha & Dwi Oktaviyanti & Euis Aqmaliyah, 2021. "Business And Financial Cycle In Indonesia: An Integrated Approach," Working Papers WP/05/2021, Bank Indonesia.
    6. Remzi Baris Tercioglu, 2020. "A sectoral approach to measuring output gap: Evidence from 20 US sectors over 1948-2019," Working Papers 2012, New School for Social Research, Department of Economics, revised Jun 2021.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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