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Information-driven Business Cycles: A Primal Approach

Author

Listed:
  • Ryan Chahrour

    () (Boston College)

  • Robert Ulbricht

    (Toulouse School of Economics)

Abstract

We develop a methodology to estimate DSGE models with incomplete information, free of parametric restrictions on information structures. First, we define a “primal” economy in which deviations from full information are captured by wedges in agents’ equilibrium expectations. Second, we provide implementability conditions, which ensure the existence of an information structure that implements these wedges. We apply the approach to estimate a New Keynesian model in which firms, households and the monetary authority have dispersed information about business conditions and productivity is the only aggregate fundamental. The estimated model fits the data remarkably well, with informational shocks able to account for the majority of U.S. business cycles. Output is driven mainly by household sentiments, whereas firm errors largely determine inflation. Our estimation indicates that firms and the central bank learn the aggregate state of the economy quickly, while household confusion about aggregate conditions is sizable and persistent.

Suggested Citation

  • Ryan Chahrour & Robert Ulbricht, 2017. "Information-driven Business Cycles: A Primal Approach," Boston College Working Papers in Economics 925, Boston College Department of Economics.
  • Handle: RePEc:boc:bocoec:925
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    References listed on IDEAS

    as
    1. Bergemann, Dirk & Morris, Stephen, 2016. "Bayes correlated equilibrium and the comparison of information structures in games," Theoretical Economics, Econometric Society, vol. 11(2), May.
    2. repec:oup:restud:v:82:y:2015:i:4:p:1502-1532. is not listed on IDEAS
    3. Bartosz Maćkowiak & Mirko Wiederholt, 2015. "Business Cycle Dynamics under Rational Inattention," Review of Economic Studies, Oxford University Press, vol. 82(4), pages 1502-1532.
    4. Gorodnichenko, Yuriy & Ng, Serena, 2010. "Estimation of DSGE models when the data are persistent," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 325-340, April.
    5. Todd Walker & Giacomo Rondina, 2017. "Confounding Dynamics," 2017 Meeting Papers 525, Society for Economic Dynamics.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Information-driven Business Cycles: A Primal Approach
      by Christian Zimmermann in NEP-DGE blog on 2017-04-12 08:14:56

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    Cited by:

    1. Chahrour, Ryan & Gaballo, Gaetano, 2017. "Learning from prices: amplication and business fluctuations," Working Paper Series 2053, European Central Bank.

    More about this item

    Keywords

    Business cycles; dispersed information; DSGE models; primal approach; sentiments;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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