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Risk-Sensitive Linear Approximations

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  • Meyer-Gohde, Alexander

Abstract

I construct risk-sensitive approximations of policy functions of DSGE models around the stochastic steady state and ergodic mean that are linear in the state variables. The method requires only the solution of linear equations using standard perturbation output to construct the approximation and is uniformly more accurate than standard linear approximations. In an application to real business cycles with recursive utility and growth risk, the approximation successfully estimates risk aversion using the Kalman filter, where a standard linear approximation provides no information and alternative methods require computationally intensive procedures such as particle filters. At the posterior mode, the model s market price of risk is brought in line with the postwar US Sharpe ratio without compromising the fit of the macroeconomy.

Suggested Citation

  • Meyer-Gohde, Alexander, 2015. "Risk-Sensitive Linear Approximations," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113057, Verein für Socialpolitik / German Economic Association.
  • Handle: RePEc:zbw:vfsc15:113057
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    References listed on IDEAS

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    1. van Binsbergen, Jules H. & Fernández-Villaverde, Jesús & Koijen, Ralph S.J. & Rubio-Ramírez, Juan, 2012. "The term structure of interest rates in a DSGE model with recursive preferences," Journal of Monetary Economics, Elsevier, vol. 59(7), pages 634-648.
    2. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
    3. Juillard Michel, 2011. "Local approximation of DSGE models around the risky steady state," wp.comunite 0087, Department of Communication, University of Teramo.
    4. Hong Lan & Alexander Meyer-Gohde, 2013. "Decomposing Risk in Dynamic Stochastic General Equilibrium," SFB 649 Discussion Papers SFB649DP2013-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Bidder, R.M. & Smith, M.E., 2012. "Robust animal spirits," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 738-750.
    6. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
    7. Judd, Kenneth L. & Guu, Sy-Ming, 1997. "Asymptotic methods for aggregate growth models," Journal of Economic Dynamics and Control, Elsevier, vol. 21(6), pages 1025-1042, June.
    8. Kliem, Martin & Uhlig, Harald, 2013. "Bayesian estimation of a DSGE model with asset prices," Discussion Papers 37/2013, Deutsche Bundesbank.
    9. de Groot, Oliver, 2013. "Computing the risky steady state of DSGE models," Economics Letters, Elsevier, vol. 120(3), pages 566-569.
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    Cited by:

    1. Martin Kliem & Alexander Meyer-Gohde, 2017. "(Un)expected Monetary Policy Shocks and Term Premia," SFB 649 Discussion Papers SFB649DP2017-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Pierlauro Lopez & David Lopez-Salido & Francisco Vazquez-Grande, 2018. "Risk-Adjusted Linearizations of Dynamic Equilibrium Models," Working papers 702, Banque de France.
    3. Timothy S. Hills & Taisuke Nakata & Sebastian Schmidt, 2016. "The Risky Steady State and the Interest Rate Lower Bound," Finance and Economics Discussion Series 2016-9, Board of Governors of the Federal Reserve System (U.S.).

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    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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