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A New Way to Quantify the Effect of Uncertainty

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Abstract

This paper develops a new way to quantify the effect of uncertainty and other higher-order moments. First, we estimate a nonlinear model using Bayesian methods with data on uncertainty, in addition to common macro time series. This key step allows us to decompose the exogenous and endogenous sources of uncertainty, calculate the effect of volatility following the cost of business cycles literature, and generate data-driven policy functions for any higherorder moment. Second, we use the Euler equation to analytically decompose consumption into several terms--expected consumption, the ex-ante real interest rate, and the ex-ante variance and skewness of future consumption, technology growth, and inflation--and then use the policy functions to filter the data and create a time series for the effect of each term. We apply our method to a familiar New Keynesian model with a zero lower bound constraint on the nominal interest rate and two stochastic volatility shocks, but it is adaptable to a broad class of models.

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  • Alexander W. Richter & Nathaniel A. Throckmorton, 2017. "A New Way to Quantify the Effect of Uncertainty," Working Papers 1705, Federal Reserve Bank of Dallas.
  • Handle: RePEc:fip:feddwp:1705
    DOI: 10.24149/wp1705r1
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    1. Zhiguo He & Arvind Krishnamurthy, 2019. "A Macroeconomic Framework for Quantifying Systemic Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(4), pages 1-37, October.
    2. Haroon Mumtaz & Francesco Zanetti, 2013. "The Impact of the Volatility of Monetary Policy Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(4), pages 535-558, June.
    3. Oliver de Groot & Alexander W. Richter & Nathaniel A. Throckmorton, 2018. "Uncertainty Shocks in a Model of Effective Demand: Comment," Econometrica, Econometric Society, vol. 86(4), pages 1513-1526, July.
    4. Michael Plante & Alexander W. Richter & Nathaniel A. Throckmorton, 2018. "The Zero Lower Bound and Endogenous Uncertainty," Economic Journal, Royal Economic Society, vol. 128(611), pages 1730-1757, June.
    5. Karen Kopecky & Richard Suen, 2010. "Finite State Markov-chain Approximations to Highly Persistent Processes," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 13(3), pages 701-714, July.
    6. Alexander Richter & Nathaniel Throckmorton & Todd Walker, 2014. "Accuracy, Speed and Robustness of Policy Function Iteration," Computational Economics, Springer;Society for Computational Economics, vol. 44(4), pages 445-476, December.
    7. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    8. Pablo D. Fajgelbaum & Edouard Schaal & Mathieu Taschereau-Dumouchel, 2017. "Uncertainty Traps," The Quarterly Journal of Economics, Oxford University Press, vol. 132(4), pages 1641-1692.
    9. Straub, Ludwig & Ulbricht, Robert, 2015. "Endogenous Uncertainty and Credit Crunches," TSE Working Papers 15-604, Toulouse School of Economics (TSE), revised Dec 2017.
    10. Edward P. Herbst & Frank Schorfheide, 2016. "Bayesian Estimation of DSGE Models," Economics Books, Princeton University Press, edition 1, number 10612.
    11. William B. Peterman, 2016. "Reconciling Micro And Macro Estimates Of The Frisch Labor Supply Elasticity," Economic Inquiry, Western Economic Association International, vol. 54(1), pages 100-120, January.
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    Cited by:

    1. Oliver de Groot & Alexander W. Richter & Nathaniel A. Throckmorton, 2018. "Uncertainty Shocks in a Model of Effective Demand: Comment," Econometrica, Econometric Society, vol. 86(4), pages 1513-1526, July.
    2. Grzegorz Długoszek, 2018. "Macroeconomic Effects of Financial Uncertainty," 2018 Meeting Papers 1128, Society for Economic Dynamics.
    3. Saltzman, Bennett & Yung, Julieta, 2018. "A machine learning approach to identifying different types of uncertainty," Economics Letters, Elsevier, vol. 171(C), pages 58-62.

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    More about this item

    Keywords

    Endogenous uncertainty; stochastic volatility; particle filter; zero lower bound;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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