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Using R to teach econometrics

Author

Listed:
  • Jeff Racine

    (Department of Economics, University of South Florida Tampa, FL, 33620 USA)

  • Rob Hyndman

    (Department of Econometrics & Business Statistics, Monash University, Melbourne, VIC 3800, Australia)

Abstract

R, an open-source programming environment for data analysis and graphics, has in only a decade grown to become a de-facto standard for statistical analysis against which many popular commercial programs may be measured. The use of R for the teaching of econometric methods is appealing. It provides cutting-edge statistical methods which are, by R's open-source nature, available immediately. The software is stable, available at no cost, and exists for a number of platforms, including various flavours of Unix and Linux, Windows (9x|NT|2000), and the MacOS. Manuals are also available for download at no cost, and there is extensive on-line information for the novice user. This review focuses on using R for teaching econometrics. Since R is an extremely powerful environment, this review should also be of interest to researchers. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Jeff Racine & Rob Hyndman, 2002. "Using R to teach econometrics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 175-189.
    • Handle: RePEc:jae:japmet:v:17:y:2002:i:2:p:175-189
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    File URL: http://qed.econ.queensu.ca:80/jae/2002-v17.2/
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    References listed on IDEAS

    as
    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    2. Cribari-Neto, Francisco & Zarkos, Spyros G, 1999. "R: Yet Another Econometric Programming Environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(3), pages 319-329, May-June.
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    Citations

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    Cited by:

    1. Miguel Rodrigues, 2005. "Regression with R," Econometrics 0508016, University Library of Munich, Germany.
    2. A. Talha Yalta & Riccardo Lucchetti, 2008. "The GNU|Linux platform and freedom respecting software for economists," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 279-286.
    3. J. Wilson Mixon Jr & Ryan J. Smith, 2006. "Teaching undergraduate econometrics with GRETL," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 1103-1107, November.
    4. Kurt Hornik & Friedrich Leisch & Christian Kleiber & Achim Zeileis, 2005. "Monitoring structural change in dynamic econometric models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 99-121.
    5. Shahram Amini & Christopher F. Parmeter, 2011. "A Review of the `BMS' Package for R," Working Papers 2011-8, University of Miami, Department of Economics.
    6. Wilson, Paul W., 2008. "FEAR: A software package for frontier efficiency analysis with R," Socio-Economic Planning Sciences, Elsevier, vol. 42(4), pages 247-254, December.
    7. Robert Finger, 2010. "Review of ‘Robustbase’ software for R," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(7), pages 1205-1210, November/.
    8. Zeileis, Achim, 2006. "Implementing a class of structural change tests: An econometric computing approach," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 2987-3008, July.
    9. Christine Choirat & Raffello Seri, 2009. "Econometrics with Python," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 698-704.
    10. Jinhu Li & Jeffrey S. Racine, 2008. "Maxima: An open source computer algebra system," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(4), pages 515-523.
    11. Giovanni Baiocchi, 2007. "Reproducible research in computational economics: guidelines, integrated approaches, and open source software," Computational Economics, Springer;Society for Computational Economics, vol. 30(1), pages 19-40, August.

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    More about this item

    JEL classification:

    • A23 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Graduate
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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