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Information-driven Business Cycles: A Primal Approach

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  • Chahrour, Ryan
  • Ulbricht, Robert

Abstract

We develop a methodology to characterize equilibrium in DSGE models, free of parametric restrictions on information. First, we define a “primal” economy in which deviations from full information are captured by wedges in agents' expectations. Then, we provide conditions ensuring some information-structure can implement these wedges. We apply the approach to estimate a business cycle model where firms and households have dispersed information. The estimated model fits the data, attributing the majority of fluctuations to a single shock to households' expectations. The responses are consistent with an implementation in which households become optimistic about local productivities and gradually learn about others' optimism.

Suggested Citation

  • 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.
    • Handle: RePEc:tse:wpaper:31575
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    References listed on IDEAS

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    1. 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.
    2. Jess Benhabib & Pengfei Wang & Yi Wen, 2015. "Sentiments and Aggregate Demand Fluctuations," Econometrica, Econometric Society, vol. 83, pages 549-585, March.
    3. Christian Hellwig & Venky Venkateswaran, 2015. "Dispersed Information, Sticky Prices and Monetary Business Cycles: A Hayekian Perspective," Working Papers 15-12, New York University, Leonard N. Stern School of Business, Department of Economics.
    4. Acharya, Sushant & Benhabib, Jess & Huo, Zhen, 2021. "The anatomy of sentiment-driven fluctuations," Journal of Economic Theory, Elsevier, vol. 195(C).
    5. 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.
    6. Kevin X.D. Huang & Zheng Liu & Louis Phaneuf, 2004. "Why Does the Cyclical Behavior of Real Wages Change Over Time?," American Economic Review, American Economic Association, vol. 94(4), pages 836-856, September.
    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.
    13. 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.
    14. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
    15. Todd Walker & Giacomo Rondina, 2017. "Confounding Dynamics," 2017 Meeting Papers 525, Society for Economic Dynamics.
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    Cited by:

    1. Ryan Chahrour & Kristoffer Nimark & Stefan Pitschner, 2021. "Sectoral Media Focus and Aggregate Fluctuations," American Economic Review, American Economic Association, vol. 111(12), pages 3872-3922, December.
    2. 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.
    3. Acharya, Sushant & Benhabib, Jess & Huo, Zhen, 2021. "The anatomy of sentiment-driven fluctuations," Journal of Economic Theory, Elsevier, vol. 195(C).
    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. Ambrocio, Gene, 2019. "Measuring household uncertainty in EU countries," Research Discussion Papers 17/2019, Bank of Finland.
    6. Ambrocio, Gene, 2019. "Measuring household uncertainty in EU countries," Bank of Finland Research Discussion Papers 17/2019, Bank of Finland.
    7. Chahrour, Ryan & Gaballo, Gaetano, 2017. "Learning from prices: amplication and business fluctuations," Working Paper Series 2053, European Central Bank.
    8. Han, Zhao & Tan, Fei & Wu, Jieran, 2022. "Analytic policy function iteration," Journal of Economic Theory, Elsevier, vol. 200(C).
    9. repec:zbw:bofrdp:2019_017 is not listed on IDEAS

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    More about this item

    Keywords

    Business cycles; dispersed information; DSGE models; primal approach; sentiments;
    All these keywords.

    JEL classification:

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

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