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Robust Predictions for DSGE Models with Incomplete Information

Author

Listed:
  • Ryan Chahrour

    (Boston College)

  • Robert Ulbricht

    (Toulouse School of Economics)

Abstract

We provide predictions for DSGE models with incomplete information that are robust across information structures. Our approach maps an incomplete-information model into a full-information economy with time-varying expectation wedges and provides conditions that ensure the wedges are rationalizable by some information structure. Using our approach, we quantify the potential importance of information as a source of business cycle fluctuations in an otherwise frictionless model. Our approach uncovers a central role for firm-specific demand shocks in supporting aggregate confidence fluctuations. Only if firms face unobserved local demand shocks can confidence fluctuations account for a significant portion of the US business cycle.

Suggested Citation

  • Ryan Chahrour & Robert Ulbricht, 2017. "Robust Predictions for DSGE Models with Incomplete Information," Boston College Working Papers in Economics 925, Boston College Department of Economics, revised 10 Jun 2021.
    • Handle: RePEc:boc:bocoec:925
    Note: previously circulated as "Information-driven Business Cycles: A Primal Approach"
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    References listed on IDEAS

    as
    1. Chahrour, Ryan & Gaballo, Gaetano, 2017. "Learning from prices: amplication and business fluctuations," Working Paper Series 2053, European Central Bank.
    2. Bartosz Maćkowiak & Mirko Wiederholt, 2015. "Business Cycle Dynamics under Rational Inattention," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1502-1532.
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    6. Chahrour, Ryan & Ulbricht, Robert, 2017. "Information-driven Business Cycles: A Primal Approach," TSE Working Papers 17-784, Toulouse School of Economics (TSE), revised Dec 2017.
    7. Ryan Chahrour & Robert Ulbricht, 2023. "Robust Predictions for DSGE Models with Incomplete Information," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(1), pages 173-208, January.
    8. Bergemann, Dirk & Morris, Stephen, 2016. "Bayes correlated equilibrium and the comparison of information structures in games," Theoretical Economics, Econometric Society, vol. 11(2), May.
    9. 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.
    10. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    11. Harald Uhlig, 2004. "Do Technology Shocks Lead to a Fall in Total Hours Worked?," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 361-371, 04/05.
    12. Hansen, Lars Peter & Sargent, Thomas J., 1981. "A note on Wiener-Kolmogorov prediction formulas for rational expectations models," Economics Letters, Elsevier, vol. 8(3), pages 255-260.
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    14. Todd Walker & Giacomo Rondina, 2017. "Confounding Dynamics," 2017 Meeting Papers 525, Society for Economic Dynamics.
    Full references (including those not matched with items on IDEAS)

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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

    Citations

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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.
    2. Tatsushi Okuda & Tomohiro Tsuruga & Francesco Zanetti, 2019. "Imperfect Information, Shock Heterogeneity, and Inflation Dynamics," IMES Discussion Paper Series 19-E-15, Institute for Monetary and Economic Studies, Bank of Japan.
    3. Chahrour, Ryan & Ulbricht, Robert, 2017. "Information-driven Business Cycles: A Primal Approach," TSE Working Papers 17-784, Toulouse School of Economics (TSE), revised Dec 2017.
    4. Ryan Chahrour & Robert Ulbricht, 2023. "Robust Predictions for DSGE Models with Incomplete Information," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(1), pages 173-208, January.
    5. repec:zbw:bofrdp:2019_017 is not listed on IDEAS
    6. Benhima, Kenza & Poilly, Céline, 2021. "Does demand noise matter? Identification and implications," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 278-295.
    7. Ambrocio, Gene, 2019. "Measuring household uncertainty in EU countries," Research Discussion Papers 17/2019, Bank of Finland.
    8. Tatsushi Okuda & Tomohiro Tsuruga & Francesco Zanetti, 2021. "Imperfect Information, Heterogeneous Demand Shocks,and Inflation Dynamics," Economics Series Working Papers 934, University of Oxford, Department of Economics.
    9. Camille Cornand & Rodolphe Dos Santos Ferreira, 2021. "Central bank’s stabilization and communication policies when firms have motivated overconfidence in their own information accuracy or processing," Working Papers 2118, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    10. Flynn, Joel P. & Sastry, Karthik A., 2023. "Strategic mistakes," Journal of Economic Theory, Elsevier, vol. 212(C).
    11. Wu, Jieran, 2022. "Comments on “Sentiments and real business cycles”," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).

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    More about this item

    Keywords

    Business cycles; DSGE models; incomplete-information; information-robust predictions;
    All these keywords.

    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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