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A Comparison of Programming Languages in Economics

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  • S. Borağan Aruoba
  • Jesús Fernández-Villaverde

Abstract

We solve the stochastic neoclassical growth model, the workhorse of modern macroeconomics, using C++11, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R. We implement the same algorithm, value function iteration with grid search, in each of the languages. We report the execution times of the codes in a Mac and in a Windows computer and briefly comment on the strengths and weaknesses of each language.

Suggested Citation

  • S. Borağan Aruoba & Jesús Fernández-Villaverde, 2014. "A Comparison of Programming Languages in Economics," NBER Working Papers 20263, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:20263
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    References listed on IDEAS

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    1. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
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    Cited by:

    1. Paulo Garrido & Pedro Campos & André Dias, 2015. "Balance Sheet Analysis Of Credit And Debt Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-18, August.
    2. Gintaras Palubeckis, 2020. "An Approach Integrating Simulated Annealing and Variable Neighborhood Search for the Bidirectional Loop Layout Problem," Mathematics, MDPI, vol. 9(1), pages 1-30, December.
    3. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, October.
    4. Thomas Kirschstein & Christian Bierwirth, 2018. "The selective Traveling Salesman Problem with emission allocation rules," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 97-124, January.

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    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • E0 - Macroeconomics and Monetary Economics - - General

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