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Using R to teach econometrics

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  • 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.

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File URL: http://qed.econ.queensu.ca:80/jae/2002-v17.2/
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 17 (2002)
Issue (Month): 2 ()
Pages: 175-189

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Handle: RePEc:jae:japmet:v:17:y:2002:i:2:p:175-189

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  1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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Cited by:
  1. A. Talha Yalta & Riccardo Lucchetti, 2010. "The GNU/Linux Platform and Freedom Respecting Software for Economists," Working Papers 1005, TOBB University of Economics and Technology, Department of Economics.
  2. Robert Finger, 2010. "Review of ‘Robustbase’ software for R," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(7), pages 1205-1210, November/.
  3. 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.
  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. 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.
  6. Ryan J. Smith & J. Wilson Mixon Jr, 2006. "Teaching undergraduate econometrics with GRETL," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 1103-1107.
  7. Christine Choirat & Raffello Seri, 2009. "Econometrics with Python," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 698-704.
  8. Giovanni Baiocchi, 2007. "Reproducible research in computational economics: guidelines, integrated approaches, and open source software," Computational Economics, Society for Computational Economics, vol. 30(1), pages 19-40, August.
  9. Miguel Rodrigues, 2005. "Regression with R," Econometrics 0508016, EconWPA.
  10. Shahram Amini & Christopher F. Parmeter, 2011. "A Review of the `BMS' Package for R," Working Papers 2011-8, University of Miami, Department of Economics.

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