[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/113888.html
   My bibliography  Save this paper

A Cointegration-based cartel screen for detecting collusion

Author

Listed:
  • Kurdoglu, Berkay
  • Yucel, Eray
Abstract
In this article, we propose a new empirical screen for detecting cartels, using the cointegration as our basis of modeling. The proposed screen is capable of identifying potential cartel behavior, indicating the strength of price adjustment among firms, and providing a basis for assessing structural change. The screen is applied to the Turkish cement market for an initial demonstration of use; we obtain promising results.

Suggested Citation

  • Kurdoglu, Berkay & Yucel, Eray, 2022. "A Cointegration-based cartel screen for detecting collusion," MPRA Paper 113888, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:113888
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/113888/1/MPRA_paper_113888.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bejger, Sylwester, 2012. "Cartel in the Indian cement industry: An attempt to identify it," Economics Discussion Papers 2012-18, Kiel Institute for the World Economy (IfW Kiel).
    2. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    3. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    4. Carsten J. Crede, 2019. "A Structural Break Cartel Screen for Dating and Detecting Collusion," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(3), pages 543-574, May.
    5. Abrantes-Metz, Rosa M. & Froeb, Luke M. & Geweke, John & Taylor, Christopher T., 2006. "A variance screen for collusion," International Journal of Industrial Organization, Elsevier, vol. 24(3), pages 467-486, May.
    6. Andreoli-Versbach, Patrick & Franck, Jens-Uwe, 2013. "Actions Speak Louder than Words: Econometric Evidence to Target Tacit Collusion in Oligopolistic Markets," Discussion Papers in Economics 16179, University of Munich, Department of Economics.
    7. Rosa Abrantes-Metz, 2014. "Recent Successes of Screens for Conspiracies and Manipulations: Why Are There Still Skeptics?â€," Antitrust Chronicle, Competition Policy International, vol. 10.
    8. Robert H. Porter & J. Douglas Zona, 1999. "Ohio School Milk Markets: An Analysis of Bidding," RAND Journal of Economics, The RAND Corporation, vol. 30(2), pages 263-288, Summer.
    9. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    10. José Manuel Ordóñez-de-Haro & José Luis Torres, 2014. "Price Hysteresis After Antitrust Enforcement: Evidence From Spanish Food Markets," Journal of Competition Law and Economics, Oxford University Press, vol. 10(1), pages 217-256.
    11. Alfred A. Haug, 2002. "Temporal Aggregation and the Power of Cointegration Tests: a Monte Carlo Study," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 399-412, September.
    12. Johannes Wachs & J'anos Kert'esz, 2019. "A network approach to cartel detection in public auction markets," Papers 1906.08667, arXiv.org.
    13. Patrick Bajari & Lixin Ye, 2003. "Deciding Between Competition and Collusion," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 971-989, November.
    14. Juan Jiménez & Jordi Perdiguero, 2012. "Does Rigidity of Prices Hide Collusion?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 41(3), pages 223-248, November.
    15. Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022. "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, vol. 105(C).
    16. Porter, Robert H & Zona, J Douglas, 1993. "Detection of Bid Rigging in Procurement Auctions," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 518-538, June.
    17. Hans W. Friederiszick & Frank P. Maier-Rigaud, 2008. "Triggering Inspections Ex Officio: Moving Beyond A Passive Eu Cartel Policy," Journal of Competition Law and Economics, Oxford University Press, vol. 4(1), pages 89-113.
    18. Engle, R. F. & Granger, C. W. J. (ed.), 1991. "Long-Run Economic Relationships: Readings in Cointegration," OUP Catalogue, Oxford University Press, number 9780198283393.
    19. Froeb, Luke M. & Koyak, Robert A. & Werden, Gregory J., 1993. "What is the effect of bid-rigging on prices?," Economics Letters, Elsevier, vol. 42(4), pages 419-423.
    20. Engle, R.F. & Yoo, B.S., 1989. "Cointegrated Economic Time Series: A Survey With New Results," Papers 8-89-13, Pennsylvania State - Department of Economics.
    Full references (including those not matched with items on IDEAS)

    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. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).
    2. Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
    3. Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
    4. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    5. Granlund, David & Rudholm, Niklas, 2023. "Calculating the probability of collusion based on observed price patterns," Umeå Economic Studies 1014, Umeå University, Department of Economics, revised 13 Oct 2023.
    6. Mats A. Bergman & Johan Lundberg & Sofia Lundberg & Johan Y. Stake, 2020. "Interactions Across Firms and Bid Rigging," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(1), pages 107-130, February.
    7. David Imhof & Yavuz Karagök & SAMUEL RUTZ, 2017. "Screening for Bid-rigging. Does it Work?," Working Papers 2017-09, CRESE.
    8. Huber, Martin & Imhof, David, 2023. "Flagging cartel participants with deep learning based on convolutional neural networks," International Journal of Industrial Organization, Elsevier, vol. 89(C).
    9. Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022. "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, vol. 105(C).
    10. Imhof, David, 2017. "Simple Statistical Screens to Detect Bid Rigging," FSES Working Papers 484, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    11. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for Collusion in Wholesale Electricity Markets: A Review of the Literature," Working Papers 2023-7, University of Alberta, Department of Economics.
    12. Clark, Robert & Coviello, Decio & de Leverano, Adriano, 2020. "Complementary bidding and the collusive arrangement: Evidence from an antitrust investigation," ZEW Discussion Papers 20-052, ZEW - Leibniz Centre for European Economic Research.
    13. Martin Huber & David Imhof & Rieko Ishii, 2022. "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
    14. Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(C).
    15. Imhof, David & Karagök, Yavuz & Rutz, Samuel, 2016. "Screening for bid-rigging - does it work?," FSES Working Papers 468, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    16. R. Santos Alimi, 2014. "ARDL Bounds Testing Approach to Cointegration: A Re-Examination of Augmented Fisher Hypothesis in an Open Economy," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 2(2), pages 103-114, June.
    17. Muhammad Shahbaz & Mete Feridun, 2012. "Electricity consumption and economic growth empirical evidence from Pakistan," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(5), pages 1583-1599, August.
    18. Levent KORAP, 2008. "Exchange Rate Determination Of Tl/Us$:A Co-Integration Approach," Istanbul University Econometrics and Statistics e-Journal, Department of Econometrics, Faculty of Economics, Istanbul University, vol. 7(1), pages 24-50, May.
    19. Ekundayo P. Mesagan & Isaac A. Ogbuji & Yasiru O. Alimi & Anthonia T. Odeleye, 2019. "Growth Effects of Financial Market Instruments: The Ghanaian Experience," Working Papers 19/095, European Xtramile Centre of African Studies (EXCAS).
    20. Debi P Bal & Badri N Rath, 2019. "Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India - A Reassessment," Economics Bulletin, AccessEcon, vol. 39(1), pages 592-604.

    More about this item

    Keywords

    Antitrust; Cartel; Detection; Empirical screen;
    All these keywords.

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:113888. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.