Perturbating and Estimating DSGE Models in Julia

Alvaro Salazar-Perez () and Hernán D. Seoane ()
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Alvaro Salazar-Perez: Universidad Carlos III de Madrid
Hernán D. Seoane: Universidad Carlos III de Madrid

Computational Economics, 2025, vol. 65, issue 4, No 20, 2379-2396

Abstract: 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.

Keywords: Perturbation solution; Sequential Montecarlo; Julia programming (search for similar items in EconPapers)
JEL-codes: C63 E0 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10632-2

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