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Perturbating and Estimating DSGE Models in Julia

Author

Listed:
  • Alvaro Salazar-Perez

    (Universidad Carlos III de Madrid)

  • Hernán D. Seoane

    (Universidad Carlos III de Madrid)

Abstract

This paper illustrates the power of Julia language for the solution and estimation of Dynamic Stochastic General Equilibrium models. We document large gains of the Julia implementation of Perturbation solution (first and higher orders) and Bayesian estimation using two workhorse models in the literature: the Real Business Cycle Model and a medium scale New-Keynesian Model. We release a companion package that implements 1st, 2nd a 3rd order approximation of Dynamic Stochastic General Equilibrium models and allows for estimation of (log-)linearized models using Sequential Monte-Carlo Methods. Our examples highlight that Julia has low entry costs and it is a language where it is easy to deal with parallelization.

Suggested Citation

  • Alvaro Salazar-Perez & Hernán D. Seoane, 2025. "Perturbating and Estimating DSGE Models in Julia," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2379-2396, April.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:4:d:10.1007_s10614-024-10632-2
    DOI: 10.1007/s10614-024-10632-2
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    References listed on IDEAS

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    More about this item

    Keywords

    Perturbation solution; Sequential Montecarlo; Julia programming;
    All these keywords.

    JEL classification:

    • E0 - Macroeconomics and Monetary Economics - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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