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Recoverability

Author

Listed:
  • Kyle Jurado

    (Duke University)

  • Ryan Chahrour

    (Boston College)

Abstract

When can structural shocks be recovered from observable data? We present a necessary and sufficient condition that gives the answer for any linear model. Invertibility, which requires that shocks be recoverable from current and past data only, is sufficient but not necessary. This means that semi-structural empirical methods like structural vector autoregression analysis can be applied even to models with non-invertible shocks. We illustrate these results in the context of a simple model of consumption determination with productivity shocks and non-productivity noise shocks. In an application to postwar U.S. data, we find that non-productivity shocks account for a large majority of fluctuations in aggregate consumption over business cycle frequencies.

Suggested Citation

  • Kyle Jurado & Ryan Chahrour, 2018. "Recoverability," 2018 Meeting Papers 320, Society for Economic Dynamics.
  • Handle: RePEc:red:sed018:320
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    References listed on IDEAS

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    1. Eric R. Sims, 2012. "News, Non-Invertibility, and Structural VARs," Advances in Econometrics, in: DSGE Models in Macroeconomics: Estimation, Evaluation, and New Developments, pages 81-135, Emerald Group Publishing Limited.
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    6. Mario Forni & Luca Gambetti & Marco Lippi & Luca Sala, 2017. "Noisy News in Business Cycles," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(4), pages 122-152, October.
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    10. Ryan Chahrour & Kyle Jurado, 2018. "News or Noise? The Missing Link," American Economic Review, American Economic Association, vol. 108(7), pages 1702-1736, July.
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    Cited by:

    1. Silvia Miranda-Agrippino & Giovanni Ricco, 2018. "Identification with External Instruments in Structual VARs under partial invertibility," Documents de Travail de l'OFCE 2018-24, Observatoire Francais des Conjonctures Economiques (OFCE).
    2. Paul Levine & Joseph Pearlman & Stephen Wright & Bo Yang, 2019. "Information, VARs and DSGE Models," School of Economics Discussion Papers 1619, School of Economics, University of Surrey.
    3. Nikolay Iskrev, 2021. "Spectral decomposition of the information about latent variables in dynamic macroeconomic models," Working Papers w202105, Banco de Portugal, Economics and Research Department.
    4. Majid M. Al-Sadoon, 2020. "The Spectral Approach to Linear Rational Expectations Models," Papers 2007.13804, arXiv.org, revised Aug 2024.
    5. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2019. "Identification with External Instruments in Structural VARs under Partial Invertibility," The Warwick Economics Research Paper Series (TWERPS) 1213, University of Warwick, Department of Economics.

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    More about this item

    JEL classification:

    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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