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Learning the Hard Way: Expectations and the U.S. Great Depression

Author

Listed:
  • Pablo Aguilar

    (Bank of Spain and UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES))

  • Luca Pensieroso

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES))

Abstract

We introduce adaptive learning – a parsimonious, convenient way to model uncertainty – in a dynamic general equilibrium model of the U.S. Great Depression. We show that even the smallest departure from rational expectations increases significantly the data mimicking ability of the model, in particular for what concerns the lack of recovery in detrended GDP after 1933. We conclude that in the case of big, traumatic events like the Great Depression, uncertainty is particularly unfavourable to the recovery phase.

Suggested Citation

  • Pablo Aguilar & Luca Pensieroso, 2022. "Learning the Hard Way: Expectations and the U.S. Great Depression," LIDAM Discussion Papers IRES 2022004, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    • Handle: RePEc:ctl:louvir:2022004
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    References listed on IDEAS

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    1. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
    2. Mark Gertler, 2017. "Rethinking the Power of Forward Guidance: Lessons from Japan," IMES Discussion Paper Series 17-E-08, Institute for Monetary and Economic Studies, Bank of Japan.
    3. Peter Temin, 2008. "Real Business Cycle Views of the Great Depression and Recent Events: A Review of Timothy J. Kehoe and Edward C. Prescott's Great Depressions of the Twentieth Century," Journal of Economic Literature, American Economic Association, vol. 46(3), pages 669-684, September.
    4. Christiano, Lawrence J & Eichenbaum, Martin, 1992. "Current Real-Business-Cycle Theories and Aggregate Labor-Market Fluctuations," American Economic Review, American Economic Association, vol. 82(3), pages 430-450, June.
    5. Stefano Eusepi & Bruce Preston, 2011. "Expectations, Learning, and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 101(6), pages 2844-2872, October.
    6. Bruce Preston, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    7. Carceles-Poveda, Eva & Giannitsarou, Chryssi, 2007. "Adaptive learning in practice," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2659-2697, August.
    8. De Vroey Michel R & Pensieroso Luca, 2006. "Real Business Cycle Theory and the Great Depression: The Abandonment of the Abstentionist Viewpoint," The B.E. Journal of Macroeconomics, De Gruyter, vol. 6(1), pages 1-26, November.
    9. Ben S. Bernanke & Michael T. Kiley & John M. Roberts, 2019. "Monetary Policy Strategies for a Low-Rate Environment," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 421-426, May.
    10. Harold L. Cole & Lee E. Ohanian, 1999. "The Great Depression in the United States from a neoclassical perspective," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 23(Win), pages 2-24.
    11. Sergey Slobodyan & Raf Wouters, 2012. "Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 4(2), pages 65-101, April.
    12. Lucas, Robert E., 1977. "Understanding business cycles," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 5(1), pages 7-29, January.
    13. Greenwood, Jeremy & Hercowitz, Zvi & Huffman, Gregory W, 1988. "Investment, Capacity Utilization, and the Real Business Cycle," American Economic Review, American Economic Association, vol. 78(3), pages 402-417, June.
    14. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    15. Gauti B. Eggertsson, 2008. "Great Expectations and the End of the Depression," American Economic Review, American Economic Association, vol. 98(4), pages 1476-1516, September.
    16. Mark Gertler, 2017. "Rethinking the Power of Forward Guidance: Lessons from Japan Keynote Speech by Mark Gertler," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 35, pages 39-58, November.
    17. Joines, Douglas H, 1981. "Estimates of Effective Marginal Tax Rates on Factor Incomes," The Journal of Business, University of Chicago Press, vol. 54(2), pages 191-226, April.
    18. Harrison, Sharon G. & Weder, Mark, 2006. "Did sunspot forces cause the Great Depression?," Journal of Monetary Economics, Elsevier, vol. 53(7), pages 1327-1339, October.
    19. Luca Pensieroso, 2007. "Real Business Cycle Models Of The Great Depression: A Critical Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 21(1), pages 110-142, February.
    20. Bray, Margaret M & Savin, Nathan E, 1986. "Rational Expectations Equilibria, Learning, and Model Specification," Econometrica, Econometric Society, vol. 54(5), pages 1129-1160, September.
    21. Marimon, Ramon & Scott, Andrew (ed.), 1999. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, number 9780198294979.
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    Cited by:

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    JEL classification:

    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • N10 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - General, International, or Comparative

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