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Confounding Dynamics

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  • Todd Walker

    (Indiana University)

Abstract

In the context of a dynamic model with incomplete information, we isolate a novel mechanism of shock propagation that results in waves of optimism and pessimism along a Rational Expectations equilibrium. We term the mechanism confounding dynamics because it arises from agents’ optimal signal extraction efforts on variables whose dynamics—as opposed to superimposed noise—prevents full revelation of information. Employing methods in the space of analytic functions, we are able to obtain analytical characterizations of the equilibria that generalize the celebrated Hansen-Sargent optimal prediction formula. We apply our results to a canonical real business cycle model and derive the analytic solution for output, consumption and capital. We show that, in response to a permanent positive productivity shock, confounding dynamics generate expansions and recessions that would not be present under complete information.

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  • Todd Walker, 2017. "Confounding Dynamics," 2017 Meeting Papers 141, Society for Economic Dynamics.
  • Handle: RePEc:red:sed017:141
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    References listed on IDEAS

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    Cited by:

    1. Jianjun Miao & Jieran Wu & Eric Young, 2016. "Macro-Financial Volatility under Dispersed Information," Boston University - Department of Economics - Working Papers Series WP2019-12, Boston University - Department of Economics, revised May 2019.
    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. Falck, Elisabeth & Hoffmann, Mathias & Hürtgen, Patrick, 2017. "Disagreement and monetary policy," Discussion Papers 29/2017, Deutsche Bundesbank.
    4. Acharya, Sushant & Benhabib, Jess & Huo, Zhen, 2021. "The anatomy of sentiment-driven fluctuations," Journal of Economic Theory, Elsevier, vol. 195(C).
    5. Kwangyong Park, 2018. "Central Bank Credibility and Monetary Policy," Working Papers 2018-45, Economic Research Institute, Bank of Korea.
    6. Eric M. Leeper, 2015. "Fiscal Analysis is Darned Hard," CAEPR Working Papers 2015-021, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    7. Angeletos, G.-M. & Lian, C., 2016. "Incomplete Information in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1065-1240, Elsevier.
    8. 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.
    9. Hoffmann, Mathias & Hürtgen, Patrick, 2016. "Inflation expectations, disagreement, and monetary policy," Economics Letters, Elsevier, vol. 146(C), pages 59-63.
    10. George-Marios Angeletos & Chen Lian, 2016. "Incomplete Information in Macroeconomics: Accommodating Frictions in Coordination," NBER Working Papers 22297, National Bureau of Economic Research, Inc.
    11. Paul Levine & Joseph Pearlman & Stephen Wright & Bo Yang, 2019. "Information, VARs and DSGE Models," School of Economics Discussion Papers 1619, School of Economics, University of Surrey.
    12. Jonathan J Adams, 2019. "Macroeconomic Models with Incomplete Information and Endogenous Signals," Working Papers 001004, University of Florida, Department of Economics.

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