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A Comparison Of Numerical Methods For The Solution Of Continuous-Time Dsge Models

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  • Parra-Alvarez, Juan Carlos

Abstract

This study evaluates the accuracy of a set of techniques that approximate the solution of continuous-time Dynamic Stochastic General Equilibrium models. Using the neoclassical growth model, I compare linear-quadratic, perturbation, and projection methods. All techniques are applied to the Hamilton–Jacobi–Bellman equation and the optimality conditions that define the general equilibrium of the economy. Two cases are studied depending on whether a closed-form solution is available. I also analyze how different degrees of non-linearities affect the approximated solution. The results encourage the use of perturbations for reasonable values of the structural parameters of the model and suggest the use of projection methods when a high degree of accuracy is required.

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  • Parra-Alvarez, Juan Carlos, 2018. "A Comparison Of Numerical Methods For The Solution Of Continuous-Time Dsge Models," Macroeconomic Dynamics, Cambridge University Press, vol. 22(6), pages 1555-1583, September.
    • Handle: RePEc:cup:macdyn:v:22:y:2018:i:06:p:1555-1583_00
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. A comparison of numerical methods for the solution of continuous-time DSGE models
      by Christian Zimmermann in NEP-DGE blog on 2013-12-03 09:38:34

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    Cited by:

    1. 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).
    2. Grüne, Lars & Semmler, Willi & Stieler, Marleen, 2015. "Using nonlinear model predictive control for dynamic decision problems in economics," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 112-133.
    3. 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.
    4. Burkhard Heer & Alfred Maußner, 2024. "Dynamic General Equilibrium Modeling," Springer Texts in Business and Economics, Springer, edition 3, number 978-3-031-51681-8, August.
    5. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.

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    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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