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System reduction of dynamic stochastic general equilibrium models solved by gensys

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  • Lee, Jae Won
  • Park, Woong Yong

Abstract

We propose a simple system reduction method to efficiently evaluate the likelihood of a linear dynamic stochastic general equilibrium (DSGE) model solved by the solution method of Sims (2002) or gensys. Since Sims (2002) decouples the stable and unstable block of a DSGE model and solves the unstable block forward as a constant, we do not need to track the dynamics of the unstable block, which effectively reduces the dimension of the model. The present method reduces time to evaluate the likelihood of popular DSGE models by 8.9% to 28.8% compared to the standard method without system reduction. The implementation cost of the present method is almost nil.

Suggested Citation

  • Lee, Jae Won & Park, Woong Yong, 2021. "System reduction of dynamic stochastic general equilibrium models solved by gensys," Economics Letters, Elsevier, vol. 199(C).
  • Handle: RePEc:eee:ecolet:v:199:y:2021:i:c:s016517652030464x
    DOI: 10.1016/j.econlet.2020.109704
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    References listed on IDEAS

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    1. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
    2. Frank Smets & Joris Tielens & Jan Van Hove, 2018. "Pipeline Pressures and Sectoral Inflation Dynamics," Working Paper Research 351, National Bank of Belgium.
    3. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    4. Carlos Carvalho & Jae Won Lee & Woong Yong Park, 2021. "Sectoral Price Facts in a Sticky-Price Model," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 216-256, January.
    5. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    6. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, vol. 48(5), pages 1305-1311, July.
    7. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
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    More about this item

    Keywords

    Dynamic stochastic general equilibrium models; Gensys; Likelihood; State space representation;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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