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Aggregate Skewness and the Business Cycle

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  • Iseringhausen, Martin
  • Petrella, Ivan
  • Theodoridis, Konstantinos

Abstract

We develop a data-rich measure of expected macroeconomic skewness in the US economy. Expected macroeconomic skewness is strongly procyclical, mainly reflects the cyclicality in the skewness of real variables, is highly correlated with the cross-sectional skewness of firm-level employment growth, and is distinct from financial market skewness. Revisions in expected skewness deliver dynamics that are nearly indistinguishable from those produced by the main business cycle shock of Angeletos et al. (2020). This result is robust to controlling for macroeconomic volatility and uncertainty, and alternative macroeconomic shocks. Our findings highlight the importance of higher-order dynamics for business cycle theories.

Suggested Citation

  • Iseringhausen, Martin & Petrella, Ivan & Theodoridis, Konstantinos, 2022. "Aggregate Skewness and the Business Cycle," CEPR Discussion Papers 17162, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:17162
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    1. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).

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    More about this item

    Keywords

    Asymmetry; Principal component analysis; Quantile regression; Var;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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