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Measuring Macroeconomic Stars: A Framework with Scarring Effects

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Abstract

Potential output and the natural rate of unemployment are commonly estimated through trend-cycle decompositions, where they are identified as underlying trends reflecting slow-moving supply factors. In this paper, we extend this framework to accommodate the possibility that cyclical disturbances affect trends endogenously through "scarring" effects. Two major changes occur relative to standard specifications. First, a significant share of business-cycle fluctuations is absorbed by endogenous movements in the trends rather than shifts in the cycle. Second, the estimated cycle--relieved of explaining the persistence in real variables--tracks inflation developments more closely, including through a steeper Phillips curve. While this steeper slope implies a strong co-movement between inflation and real activity in response to cyclical shocks, such strong co-movement is rarely apparent in the data. Consequently, the estimation shows a shift toward more sizable changes in the purely supply-driven components of the trends, mirrored by smaller innovations in the cycle process. In turn, this rebalancing entails different historical paths for the activity gaps, carrying important implications for the conduct of monetary policy.

Suggested Citation

  • Manuel González-Astudillo & Jean-Philippe Laforte & Antoine Lepetit, 2026. "Measuring Macroeconomic Stars: A Framework with Scarring Effects," Finance and Economics Discussion Series 2026-054, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:103589
    DOI: 10.17016/FEDS.2026.054
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    1. Basistha, Arabinda & Nelson, Charles R., 2007. "New measures of the output gap based on the forward-looking new Keynesian Phillips curve," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 498-511, March.
    2. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    3. Dave Reifschneider & William Wascher & David Wilcox, 2015. "Aggregate Supply in the United States: Recent Developments and Implications for the Conduct of Monetary Policy," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 63(1), pages 71-109, May.
    4. James C. Morley & Charles R. Nelson & Eric Zivot, 2003. "Why Are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP So Different?," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 235-243, May.
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    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
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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