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Learning, Confidence, and Business Cycles

Author

Listed:
  • Hikaru Saijo

    (University of California, Santa Cruz)

  • Cosmin Ilut

    (Duke University)

Abstract

In this paper we study the amplification feedback between uncertainty and economic activity. We build on van Nieuwerburgh and Veldkamp (2006) to model an economy with a procyclical signal to noise ratio used in filtering the hidden, persistent, state of technology. Recessions, caused by either fundamental supply or demand shocks, are periods where the lower production scale implies higher uncertainty in the form of a larger posterior variance. The endogenous increase in uncertainty makes agents less confident and further reduces economic activity, which gives rise to persistent and amplifying effects. We use linear methods to study the feedback effects of time-varying endogenous uncertainty and confidence in standard business cycle models. We illustrate the main qualitative implications in a stylized model and use a quantitative version to evaluate their magnitudes. We also provide an extension to a heterogeneous firm setting, whose aggregation is facilitated by the use of linear methods and where we can additionally analyze the impact of experimentation and firm-level dispersion shocks.

Suggested Citation

  • Hikaru Saijo & Cosmin Ilut, 2015. "Learning, Confidence, and Business Cycles," 2015 Meeting Papers 917, Society for Economic Dynamics.
  • Handle: RePEc:red:sed015:917
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    Cited by:

    1. Saijo, Hikaru, 2017. "The uncertainty multiplier and business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 78(C), pages 1-25.
    2. George-Marios Angeletos, 2018. "Frictional Coordination," Journal of the European Economic Association, European Economic Association, vol. 16(3), pages 563-603.
    3. Yoo, Donghoon, 2019. "Ambiguous information, permanent income, and consumption fluctuations," European Economic Review, Elsevier, vol. 119(C), pages 79-96.
    4. 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.
    5. Jochen Guentner & Elena Afanasyeva, 2017. "Noise-Ridden Lending Cycles," 2017 Meeting Papers 1211, Society for Economic Dynamics.
    6. Guangyu PEI, 2019. "Uncertainty, Pessimism and Economic Fluctuations," 2019 Meeting Papers 1494, Society for Economic Dynamics.
    7. Alexander W. Richter & Nathaniel A. Throckmorton, 2017. "A New Way to Quantify the Effect of Uncertainty," Working Papers 1705, Federal Reserve Bank of Dallas.
    8. Mirela Miescu, 2019. "Uncertainty shocks in emerging economies," Working Papers 277077821, Lancaster University Management School, Economics Department.
    9. Claudio Michelacci & Luigi Paciello, 2020. "Aggregate Risk or Aggregate Uncertainty? Evidence from UK Households," EIEF Working Papers Series 2006, Einaudi Institute for Economics and Finance (EIEF), revised Apr 2020.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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