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Learning, Confidence, and Business Cycles

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  • Cosmin L. Ilut
  • Hikaru Saijo
Abstract
We build a tractable heterogeneous-firm business cycle model where firms face Knightian uncertainty about their profitability and learn it through production. The cross-sectional mean of firm-level uncertainty is high in recessions because firms invest and hire less. The higher uncertainty reduces agents' confidence and further discourages economic activity. We characterize this feedback mechanism in linear, workhorse macroeconomic models and find that it endogenously generates empirically desirable cross-equation restrictions such as: amplified and hump-shaped dynamics, co-movement driven by demand shocks and countercyclical correlated wedges in the equilibrium conditions for labor, risk-free and risky assets. In a rich model estimated on US macroeconomic and financial data, the information friction changes inference and significantly reduces the empirical need for standard real and nominal rigidities. Furthermore, endogenous idiosyncratic uncertainty propagates shocks to financial conditions, disciplined by observed spreads, as key drivers of fluctuations, and magnifies the aggregate activity's response to monetary and fiscal policies.

Suggested Citation

  • Cosmin L. Ilut & Hikaru Saijo, 2016. "Learning, Confidence, and Business Cycles," NBER Working Papers 22958, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:22958
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    Cited by:

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    2. Xu, Xiangyun & Li, Xing & Meng, Jie & Hu, Xueqi & Ge, Yingfan, 2024. "The impact of the tail risk of demand on corporate investment: Evidence from Chinese manufacturing firms," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    3. George-Marios Angeletos, 2018. "Frictional Coordination," Journal of the European Economic Association, European Economic Association, vol. 16(3), pages 563-603.
    4. Yoo, Donghoon, 2019. "Ambiguous information, permanent income, and consumption fluctuations," European Economic Review, Elsevier, vol. 119(C), pages 79-96.
    5. 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.
    6. Jochen Guentner & Elena Afanasyeva, 2017. "Noise-Ridden Lending Cycles," 2017 Meeting Papers 1211, Society for Economic Dynamics.
    7. Guangyu PEI, 2019. "Uncertainty, Pessimism and Economic Fluctuations," 2019 Meeting Papers 1494, Society for Economic Dynamics.
    8. Alexander W. Richter & Nathaniel A. Throckmorton, 2017. "A New Way to Quantify the Effect of Uncertainty," Working Papers 1705, Federal Reserve Bank of Dallas.
    9. Mirela Miescu, 2019. "Uncertainty shocks in emerging economies," Working Papers 277077821, Lancaster University Management School, Economics Department.
    10. 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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