IDEAS home Printed from https://ideas.repec.org/a/eee/irlaec/v68y2021ics0144818821000430.html
   My bibliography  Save this article

Estimating cartel damages with model averaging approaches

Author

Listed:
  • Tsay, Wen-Jen

Abstract

This research offers an easy-to-implement forecast combination procedure to deal with issues of model uncertainty when evaluating cartel damages. We combine the Mallows model averaging (MMA) method with both the dummy variable (DV) and forecasting approaches to investigate the famous citric acid cartel case during the 1990s. The path of but-for prices generated from the MMA method with DV specification lies in-between those generated from the forecasting and DV methods, supporting the theoretical properties of the MMA method that weights over different forecasts generated from various candidate models. The findings indicate that the but-for prices generated from the MMA method could serve as a useful robustness check for cartel damage estimations.

Suggested Citation

  • Tsay, Wen-Jen, 2021. "Estimating cartel damages with model averaging approaches," International Review of Law and Economics, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:irlaec:v:68:y:2021:i:c:s0144818821000430
    DOI: 10.1016/j.irle.2021.106019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0144818821000430
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irle.2021.106019?utm_source=ideas
    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

    as
    1. Kai Hüschelrath & Kathrin Müller & Tobias Veith, 2013. "Concrete Shoes For Competition: The Effect Of The German Cement Cartel On Market Price," Journal of Competition Law and Economics, Oxford University Press, vol. 9(1), pages 97-123.
    2. Yuan, Zheng & Yang, Yuhong, 2005. "Combining Linear Regression Models: When and How?," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1202-1214, December.
    3. John M. Connor, 2004. "Global Antitrust Prosecutions of Modern International Cartels," Journal of Industry, Competition and Trade, Springer, vol. 4(3), pages 239-267, September.
    4. Joseph E. Harrington, Jr., 2004. "Cartel Pricing Dynamics in the Presence of an Antitrust Authority," RAND Journal of Economics, The RAND Corporation, vol. 35(4), pages 651-673, Winter.
    5. Yang, Yuhong, 2000. "Combining Different Procedures for Adaptive Regression," Journal of Multivariate Analysis, Elsevier, vol. 74(1), pages 135-161, July.
    6. Joseph E. Harrington, 2004. "Post‐Cartel Pricing During Litigation," Journal of Industrial Economics, Wiley Blackwell, vol. 52(4), pages 517-533, December.
    7. Salkever, David S., 1976. "The use of dummy variables to compute predictions, prediction errors, and confidence intervals," Journal of Econometrics, Elsevier, vol. 4(4), pages 393-397, November.
    8. Souam, Said, 2001. "Optimal antitrust policy under different regimes of fines," International Journal of Industrial Organization, Elsevier, vol. 19(1-2), pages 1-26, January.
    9. Bolotova, Yuliya & Connor, John M. & Miller, Douglas J., 2008. "The impact of collusion on price behavior: Empirical results from two recent cases," International Journal of Industrial Organization, Elsevier, vol. 26(6), pages 1290-1307, November.
    10. Besanko, David & Spulber, Daniel F, 1989. "Antitrust Enforcement under Asymmetric Information," Economic Journal, Royal Economic Society, vol. 99(396), pages 408-425, June.
    11. Guido Kuersteiner & Ryo Okui, 2010. "Constructing Optimal Instruments by First-Stage Prediction Averaging," Econometrica, Econometric Society, vol. 78(2), pages 697-718, March.
    12. Baker, Jonathan B & Rubinfeld, Daniel L, 1999. "Empirical Methods in Antitrust Litigation: Review and Critique," American Law and Economics Review, Oxford University Press, vol. 1(1-2), pages 386-435, Fall.
    13. James F. Nieberding, 2006. "Estimating overcharges in antitrust cases using a reduced-form approach: Methods and issues," Journal of Applied Economics, Universidad del CEMA, vol. 9, pages 361-380, November.
    14. Hans W. Friederiszick & Lars-Hendrik Röller, 2010. "Quantification Of Harm In Damages Actions For Antitrust Infringements: Insights From German Cartel Cases," Journal of Competition Law and Economics, Oxford University Press, vol. 6(3), pages 595-618.
    15. Hansen, Bruce E., 2008. "Least-squares forecast averaging," Journal of Econometrics, Elsevier, vol. 146(2), pages 342-350, October.
    16. Liao, Jen-Che & Tsay, Wen-Jen, 2020. "Optimal Multistep Var Forecast Averaging," Econometric Theory, Cambridge University Press, vol. 36(6), pages 1099-1126, December.
    17. Yang Y., 2001. "Adaptive Regression by Mixing," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 574-588, June.
    18. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
    19. James F. Nieberding, 2006. "Estimating Overcharges in Antitrust Cases Using a Reduced-Form Approach: Methods and Issues," Journal of Applied Economics, Taylor & Francis Journals, vol. 9(2), pages 361-380, November.
    20. Xinyu Zhang & Alan Wan & Sherry Zhou, 2012. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142.
    21. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
    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. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, vol. 1(2), pages 1-23, September.
    2. Zhang, Xinyu & Wan, Alan T.K. & Zou, Guohua, 2013. "Model averaging by jackknife criterion in models with dependent data," Journal of Econometrics, Elsevier, vol. 174(2), pages 82-94.
    3. Chu-An Liu & Biing-Shen Kuo & Wen-Jen Tsay, 2017. "Autoregressive Spectral Averaging Estimator," IEAS Working Paper : academic research 17-A013, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    4. Wan, Alan T.K. & Zhang, Xinyu & Wang, Shouyang, 2014. "Frequentist model averaging for multinomial and ordered logit models," International Journal of Forecasting, Elsevier, vol. 30(1), pages 118-128.
    5. Lu, Xun & Su, Liangjun, 2015. "Jackknife model averaging for quantile regressions," Journal of Econometrics, Elsevier, vol. 188(1), pages 40-58.
    6. Zhang, Xinyu & Liu, Chu-An, 2023. "Model averaging prediction by K-fold cross-validation," Journal of Econometrics, Elsevier, vol. 235(1), pages 280-301.
    7. Zhang, Xinyu & Lu, Zudi & Zou, Guohua, 2013. "Adaptively combined forecasting for discrete response time series," Journal of Econometrics, Elsevier, vol. 176(1), pages 80-91.
    8. Aman Ullah & Alan T. K. Wan & Huansha Wang & Xinyu Zhang & Guohua Zou, 2017. "A semiparametric generalized ridge estimator and link with model averaging," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 370-384, March.
    9. Shangwei Zhao, 2014. "Model averaging based on James–Stein estimators," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(8), pages 1013-1022, November.
    10. Kai Hüschelrath & Kathrin Müller & Tobias Veith, 2013. "Concrete Shoes For Competition: The Effect Of The German Cement Cartel On Market Price," Journal of Competition Law and Economics, Oxford University Press, vol. 9(1), pages 97-123.
    11. Christopher F. Parmeter & Alan T. K. Wan & Xinyu Zhang, 2019. "Model averaging estimators for the stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 51(2), pages 91-103, June.
    12. Yuting Wei & Qihua Wang & Wei Liu, 2021. "Model averaging for linear models with responses missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 535-553, June.
    13. Alena Skolkova, 2023. "Model Averaging with Ridge Regularization," CERGE-EI Working Papers wp758, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    14. Harrington, Joseph Jr. & Chen, Joe, 2006. "Cartel pricing dynamics with cost variability and endogenous buyer detection," International Journal of Industrial Organization, Elsevier, vol. 24(6), pages 1185-1212, November.
    15. Xinyu Zhang & Alan T. K. Wan & Sherry Z. Zhou, 2011. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142, June.
    16. Sun, Yuying & Hong, Yongmiao & Lee, Tae-Hwy & Wang, Shouyang & Zhang, Xinyu, 2021. "Time-varying model averaging," Journal of Econometrics, Elsevier, vol. 222(2), pages 974-992.
    17. Liu, Chu-An, 2015. "Distribution theory of the least squares averaging estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
    18. Xu Cheng & Zhipeng Liao & Ruoyao Shi, 2013. "Uniform Asymptotic Risk of Averaging GMM Estimator Robust to Misspecification, Second Version," PIER Working Paper Archive 15-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Mar 2015.
    19. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
    20. Qingfeng Liu & Ryo Okui & Arihiro Yoshimura, 2016. "Generalized Least Squares Model Averaging," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1692-1752, December.

    More about this item

    Keywords

    Cartel; Antitrust; Damage estimation;
    All these keywords.

    JEL classification:

    • L12 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Monopoly; Monopolization Strategies
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

    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:eee:irlaec:v:68:y:2021:i:c:s0144818821000430. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/irle .

    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.