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Signing Out Confounding Shocks in Variance-Maximizing Identification Methods

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  • Neville Francis
  • Gene Kindberg-Hanlon

Abstract

Recent papers have examined the dominant drivers of business cycles using variance-maximizing techniques for identification. However, identification is poor when shocks other than the target of interest play large roles in driving volatility at the targeted frequency or horizon, leading them to capture a "hybrid" shock. This paper suggests a simple fix that lowers biases in the impulse responses. The fix is to include theoretically informed sign and magnitude restrictions at the identification stage of the vector autoregression. Applying this to US data, we find an equal role for demand and supply shocks in generating business cycle fluctuations.

Suggested Citation

  • Neville Francis & Gene Kindberg-Hanlon, 2022. "Signing Out Confounding Shocks in Variance-Maximizing Identification Methods," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 476-480, May.
    • Handle: RePEc:aea:apandp:v:112:y:2022:p:476-80
    DOI: 10.1257/pandp.20221046
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    References listed on IDEAS

    as
    1. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
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    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
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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