IDEAS home Printed from
   My bibliography  Save this article

Programming Correlation Criteria with free CAS Software


  • George E. Halkos

    (University of Thessaly)

  • Kyriaki D. Tsilika

    (University of Thessaly)


Our contribution in this work is to set the directions for specialized econometric computations in a free computer algebra system, Xcas. We focus on the programming of a routine dedicated to correlation criteria for multiple regression models. We program several operations for detecting and evaluating collinearity by applying the diagnostic techniques of linear regression analysis. In order to illustrate the computational performance of our Xcas codes, we repeat most of the analysis carried out in widely used commercial software, along with some extra statistics. Xcas could constitute a supplemental tool in a collinear data study. Its use is proposed complementary to established econometric software or as substitute software.

Suggested Citation

  • 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.
  • Handle: RePEc:kap:compec:v:52:y:2018:i:1:d:10.1007_s10614-016-9604-1
    DOI: 10.1007/s10614-016-9604-1

    Download full text from publisher

    File URL:
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL:
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Kendrick, David A & Amman, Hans M, 1999. "Programming Languages in Economics," Computational Economics, Springer;Society for Computational Economics, vol. 14(1-2), pages 151-181, October.
    2. George Halkos & Kyriaki Tsilika, 2015. "Programming Identification Criteria in Simultaneous Equation Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 157-170, June.
    3. Belsley, David A, 1999. "Mathematica as an Environment for Doing Economics and Econometrics," Computational Economics, Springer;Society for Computational Economics, vol. 14(1-2), pages 69-87, October.
    4. 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.
    5. David A. Belsley, 1988. "A Guide to Using the Collinearity Diagnostics," Boston College Working Papers in Economics 190, Boston College Department of Economics.
    6. H. M. Amman & D. A. Kendrick & J. Rust (ed.), 1996. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 1, number 1.
    7. Kenneth A. Bollen & Shawn Bauldry, 2010. "Model Identification and Computer Algebra," Sociological Methods & Research, , vol. 39(2), pages 127-156, November.
    8. Hutton, John & Hutton, James, 1995. "The Maple Computer Algebra System: A Review," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(3), pages 329-337, July-Sept.
    9. A. Merckens & P. A. Bekker, 1993. "Identification of simultaneous equation models with measurement error: a computerized evaluation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 47(4), pages 233-244, December.
    10. Friendly, Michael & Kwan, Ernest, 2009. "Where's Waldo? Visualizing Collinearity Diagnostics," The American Statistician, American Statistical Association, vol. 63(1), pages 56-65.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Kimon Ntotsis & Alex Karagrigoriou & Andreas Artemiou, 2021. "Interdependency Pattern Recognition in Econometrics: A Penalized Regularization Antidote," Econometrics, MDPI, vol. 9(4), pages 1-13, December.
    2. Achilleas Anastasiou & Peter Hatzopoulos & Alex Karagrigoriou & George Mavridoglou, 2021. "Causality Distance Measures for Multivariate Time Series with Applications," Mathematics, MDPI, vol. 9(21), pages 1-15, October.
    3. Román Salmerón-Gómez & Catalina García-García & José García-Pérez, 2021. "A Guide to Using the R Package “multiColl” for Detecting Multicollinearity," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 529-536, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Halkos, George & Tsilika, Kyriaki, 2016. "Measures of correlation and computer algebra," MPRA Paper 70200, University Library of Munich, Germany.
    2. George Halkos & Kyriaki Tsilika, 2015. "Programming Identification Criteria in Simultaneous Equation Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 157-170, June.
    3. Tomasz Kopczewski, 2015. "Think not calculate! Implementation of Felix Klein postulates in economic education with CAS software," Working Papers 2015-38, Faculty of Economic Sciences, University of Warsaw.
    4. 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.
    5. 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.
    6. 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.
    7. Peter John Robinson & W.J.W. Botzen & F. Zhou, 2019. "An experimental study of charity hazard: The effect of risky and ambiguous government compensation on flood insurance demand," Working Papers 19-19, Utrecht School of Economics.
    8. Blueschke-Nikolaeva, V. & Blueschke, D. & Neck, R., 2012. "Optimal control of nonlinear dynamic econometric models: An algorithm and an application," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3230-3240.
    9. Maurizio Iacopetta, 2014. "Dynamics of assets liquidity and inequality in economies with decentralized markets," Sciences Po publications info:hdl:2441/2029nqlehl8, Sciences Po.
    10. Mutschler, Willi, 2018. "Higher-order statistics for DSGE models," Econometrics and Statistics, Elsevier, vol. 6(C), pages 44-56.
    11. Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2020. "A tractable framework for analyzing a class of nonstationary Markov models," Quantitative Economics, Econometric Society, vol. 11(4), pages 1289-1323, November.
    12. Di Nicolo, G. & Gamba, A. & Lucchetta, M., 2011. "Capital Regulation, Liquidity Requirements and Taxation in a Dynamic Model of Banking," Discussion Paper 2011-090, Tilburg University, Center for Economic Research.
    13. Noelia Caceres & Luis M. Romero & Francisco J. Morales & Antonio Reyes & Francisco G. Benitez, 2018. "Estimating traffic volumes on intercity road locations using roadway attributes, socioeconomic features and other work-related activity characteristics," Transportation, Springer, vol. 45(5), pages 1449-1473, September.
    14. Amman, Hans M. & Kendrick, David A., 1998. "Computing the steady state of linear quadratic optimization models with rational expectations," Economics Letters, Elsevier, vol. 58(2), pages 185-191, February.
    15. John Stachurski, 2009. "Economic Dynamics: Theory and Computation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012774, December.
    16. Lohano, Heman Das, 2002. "A Stochastic Dynamic Programming Analysis of Farmland Investment and Financial Management," Faculty and Alumni Dissertations 309035, University of Minnesota, Department of Applied Economics.
    17. Kenneth L. Judd & Lilia Maliar & Serguei Maliar & Inna Tsener, 2017. "How to solve dynamic stochastic models computing expectations just once," Quantitative Economics, Econometric Society, vol. 8(3), pages 851-893, November.
    18. Sergey Ivashchenko & Semih Emre Çekin & Kevin Kotzé & Rangan Gupta, 2020. "Forecasting with Second-Order Approximations and Markov-Switching DSGE Models," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 747-771, December.
    19. Velandia, Margarita & Jensen, Kimberly & DeLong, Karen L. & Wszelaki, Annette & Rihn, Alicia, 2020. "Tennessee Fruit and Vegetable Farmer Preferences and Willingness to Pay for Plastic Biodegradable Mulch," Journal of Food Distribution Research, Food Distribution Research Society, vol. 51(3), November.
    20. Hans M. Amman & Marco Paolo Tucci, 2018. "How active is active learning: value function method vs an approximation method," Department of Economics University of Siena 788, Department of Economics, University of Siena.

    More about this item


    Multicollinearity; Correlation criteria; Computational econometrics; CAS software;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software


    Access and download statistics


    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:kap:compec:v:52:y:2018:i:1:d:10.1007_s10614-016-9604-1. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: .

    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.