IDEAS home Printed from https://ideas.repec.org/p/tex/carewp/9504.html
   My bibliography  Save this paper

Programming Languages in Economics

Author

Listed:
  • Hans M. Amman

    (Eco, U. of Netherlands)

  • David A. Kendrick

    (Eco, U. of Texas)

Abstract

Young economists sometimes ask which computer programming languages they should learn. This paper answers that question by suggesting that they begin with a high level language like GAUSS, GAMS, Mathematica, Maple or MATLAB depending on their field of specialization in economics. Then they should work down to one of the low level languages such as Fortran, Basic, C, C++ or Java depending on the planned areas of application. Finally, they should proceed to the languages which are used to develop graphical interfaces and internet applications, viz. Visual Basic, C. C++ or Java. Citation Copyright 1999 by Kluwer Academic Publishers.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Hans M. Amman & David A. Kendrick, 1995. "Programming Languages in Economics," CARE Working Papers 9504, The University of Texas at Austin, Center for Applied Research in Economics.
    • Handle: RePEc:tex:carewp:9504
    Note: None
    as

    Download full text from publisher

    File URL: http://www.eco.utexas.edu/Ecopapers/wp9504.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rodolphe Buda, 2013. "SIMUL 3.2: An Econometric Tool for Multidimensional Modelling," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 517-524, April.
    2. Buda, Rodolphe, 2005. "Numerical Analysis in Econom(etr)ic Softwares: the Data-Memory Shortage Management," MPRA Paper 9145, University Library of Munich, Germany, revised 2007.
    3. Charles G. Renfro, 2009. "The Practice of Econometric Theory," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75571-5, July-Dece.
    4. Vieira, Wilson da Cruz & Lelis, Levi H. Santana de, 2005. "Programming languages in economics: a comparison among Fortran77, C++, and Java," Revista de Economia e Agronegócio / Brazilian Review of Economics and Agribusiness, Federal University of Vicosa, Department of Agricultural Economics, vol. 3(3), pages 1-16.
    5. Francisco Cribari-Neto & Spyros Zarkos, 2003. "Econometric and Statistical Computing Using Ox," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 277-295, June.
    6. Kendrick, David A., 2005. "Stochastic control for economic models: past, present and the paths ahead," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 3-30, January.
    7. mercado, p. ruben, 2003. "Empirical economywide modeling in argentina," MPRA Paper 58611, University Library of Munich, Germany.
    8. Álvaro Andrés PERDOMO STRAUCH, 2008. "Modelo Estándar de Equilibrio General Computable," Archivos de Economía 4943, Departamento Nacional de Planeación.
    9. Halkos, George & Tsilika, Kyriaki, 2016. "Measures of correlation and computer algebra," MPRA Paper 70200, University Library of Munich, Germany.
    10. Buda, Rodolphe, 2001. "Les algorithmes de la modélisation : une analyse critique pour la modélisation économique," MPRA Paper 3926, University Library of Munich, Germany, revised Jul 2004.
    11. George E. Halkos & Kyriaki D. Tsilika, 2018. "Programming Correlation Criteria with free CAS Software," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 299-311, June.
    12. Rodolphe Buda, 2015. "Data Checking and Econometric Software Development: A Technique of Traceability by Fictive Data Encoding," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 325-357, August.
    13. Simon Peters & Ken Clark & Pascal Ekin & Anja Le Blanc & Stephen Pickles, 2007. "Grid Enabling Empirical Economics: A Microdata Application," Computational Economics, Springer;Society for Computational Economics, vol. 30(4), pages 349-370, November.

    More about this item

    Keywords

    Macroeconomics; learning; stochastic optimization; numerical experiments;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tex:carewp:9504. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Caroline Thomas (email available below). General contact details of provider: https://edirc.repec.org/data/deutxus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.