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Confidence and the Propagation of Demand Shocks

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  • George-Marios Angeletos
  • Chen Lian

Abstract

We revisit the question of why shifts in aggregate demand drive business cycles. Our theory combines intertemporal substitution in production with rational confusion, or bounded rationality, in consumption and investment. The first element allows aggregate supply to respond to shifts in aggregate demand without nominal rigidity. The second introduces a “confidence multiplier,” that is, a positive feedback loop between real economic activity, consumer expectations of permanent income, and investor expectations of returns. This mechanism amplifies the business-cycle fluctuations triggered by demand shocks (but not necessarily those triggered by supply shocks); it helps investment to comove with consumption; and it allows front-loaded fiscal stimuli to crowd in private spending.

Suggested Citation

  • George-Marios Angeletos & Chen Lian, 2020. "Confidence and the Propagation of Demand Shocks," NBER Working Papers 27702, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27702
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    References listed on IDEAS

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    1. George-Marios Angeletos & Zhen Huo & Karthik A. Sastry, 2021. "Imperfect Macroeconomic Expectations: Evidence and Theory," NBER Macroeconomics Annual, University of Chicago Press, vol. 35(1), pages 1-86.
    2. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    3. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    4. Xavier Gabaix, 2014. "A Sparsity-Based Model of Bounded Rationality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1661-1710.
    5. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1295-1328.
    6. Sophocles Mavroeidis & Mikkel Plagborg-Møller & James H. Stock, 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Journal of Economic Literature, American Economic Association, vol. 52(1), pages 124-188, March.
    7. Barberis, Nicholas & Greenwood, Robin & Jin, Lawrence & Shleifer, Andrei, 2015. "X-CAPM: An extrapolative capital asset pricing model," Journal of Financial Economics, Elsevier, vol. 115(1), pages 1-24.
    8. Diamond, Peter A, 1982. "Aggregate Demand Management in Search Equilibrium," Journal of Political Economy, University of Chicago Press, vol. 90(5), pages 881-894, October.
    9. Christopher D. Carroll & Edmund Crawley & Jiri Slacalek & Kiichi Tokuoka & Matthew N. White, 2020. "Sticky Expectations and Consumption Dynamics," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(3), pages 40-76, July.
    10. Filip Rozsypal & Kathrin Schlafmann, 2023. "Overpersistence Bias in Individual Income Expectations and Its Aggregate Implications," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(4), pages 331-371, October.
    11. George-Marios Angeletos & Chen Lian, 2017. "Dampening General Equilibrium: From Micro to Macro," NBER Working Papers 23379, National Bureau of Economic Research, Inc.
    12. George-Marios Angeletos & Zhen Huo, 2021. "Myopia and Anchoring," American Economic Review, American Economic Association, vol. 111(4), pages 1166-1200, April.
    13. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2017. "Inflation Expectations, Learning, and Supermarket Prices: Evidence from Survey Experiments," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(3), pages 1-35, July.
    14. Nir Jaimovich & Sergio Rebelo, 2009. "Can News about the Future Drive the Business Cycle?," American Economic Review, American Economic Association, vol. 99(4), pages 1097-1118, September.
    15. 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.
    16. Zhen Huo & Jose-Victor Rios-Rull, 2020. "Demand Induced Fluctuations," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 37, pages 99-117, August.
    17. R?diger Bachmann & Ricardo J. Caballero & Eduardo M. R. A. Engel, 2013. "Aggregate Implications of Lumpy Investment: New Evidence and a DSGE Model," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(4), pages 29-67, October.
    18. Pooya Molavi, 2019. "Macroeconomics with Learning and Misspecification: A General Theory and Applications," 2019 Meeting Papers 1584, Society for Economic Dynamics.
    19. George A. Akerlof & Robert J. Shiller, 2010. "Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism," Economics Books, Princeton University Press, edition 1, number 9163.
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    Cited by:

    1. Adams, Jonathan J., 2023. "Moderating noise-driven macroeconomic fluctuations under dispersed information," Journal of Economic Dynamics and Control, Elsevier, vol. 156(C).
    2. Liudmila Kitrar & Tamara Lipkind, 2021. "Development Of Composite Indicators Of Cyclical Response In Business Surveys Considering The Specifics Of The ‘Covid-19 Economy’," HSE Working papers WP BRP 121/STI/2021, National Research University Higher School of Economics.

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    More about this item

    JEL classification:

    • E03 - Macroeconomics and Monetary Economics - - General - - - Behavioral Macroeconomics
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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