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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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    Cited by:

    1. Martin Kliem & Alexander Meyer‐Gohde, 2022. "(Un)expected monetary policy shocks and term premia," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 477-499, April.
    2. Pierlauro Lopez & David Lopez-Salido & Francisco Vazquez-Grande, 2018. "Risk-Adjusted Linearizations of Dynamic Equilibrium Models," Working papers 702, Banque de France.
    3. Parra-Alvarez, Juan Carlos & Polattimur, Hamza & Posch, Olaf, 2021. "Risk matters: Breaking certainty equivalence in linear approximations," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    4. Schmidt, Sebastian & Nakata, Taisuke & Hills, Timothy, 2016. "The risky steady state and the interest rate lower bound," Working Paper Series 1913, European Central Bank.
    5. Juan Carlos Parra-Alvarez & Hamza Polattimur & Olaf Posch, 2020. "Risk Matters: Breaking Certainty Equivalence," CREATES Research Papers 2020-02, Department of Economics and Business Economics, Aarhus University.

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    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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