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Matlab, Python, Julia: What to Choose in Economics?

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  • Coleman, Chase
  • Lyon, Spencer
  • Maliar, Serguei

Abstract

We perform a comparison of Matlab, Python and Julia as programming languages to be used for implementing global nonlinear solution techniques. We consider two popular applications: a neoclassical growth model and a new Keynesian model. The goal of our analysis is twofold: First, it is aimed at helping researchers in economics to choose the programming language that is best suited to their applications and, if needed, help them transit from one programming language to another. Second, our collections of routines can be viewed as a toolbox with a special emphasis on techniques for dealing with high dimensional economic problems. We provide the routines in the three languages for constructing random and quasi-random grids, low-cost monomial integration, various global solution methods, routines for checking the accuracy of the solutions, etc. Our global solution methods are not only accurate but also fast. Solving a new Keynesian model with eight state variables only takes a few seconds, even in the presence of active zero lower bound on nominal interest rates. This speed is important because it then allows the model to be solved repeatedly as one would require in order to do estimation.

Suggested Citation

  • Coleman, Chase & Lyon, Spencer & Maliar, Serguei, 2018. "Matlab, Python, Julia: What to Choose in Economics?," CEPR Discussion Papers 13210, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:13210
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    Cited by:

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    2. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.
    3. Hoang Van Khieu & Roberto Leon-Gonzalez, 2024. "The distributions of annual earnings and interest rates in a continuous time Markov chain economy," Economics Bulletin, AccessEcon, vol. 44(3), pages 1096-1108.
    4. Maliar, Lilia & Maliar, Serguei & Winant, Pablo, 2021. "Deep learning for solving dynamic economic models," Journal of Monetary Economics, Elsevier, vol. 122(C), pages 76-101.
    5. Duarte, Victor & Duarte, Diogo & Fonseca, Julia & Montecinos, Alexis, 2020. "Benchmarking machine-learning software and hardware for quantitative economics," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    6. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.

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    More about this item

    Keywords

    Toolkit; Matlab; Python; Julia; Dynamic programming; Global solution; Nonlinear; High dimensionality; Large scale; Value function iteration;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • 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
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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