IDEAS home Printed from https://ideas.repec.org/p/cem/doctra/276.html
   My bibliography  Save this paper

Econometric Estimation of PCAIDS Models

Author

Listed:
  • Germán Coloma

Abstract

This paper presents a version of the proportionally calibrated almost ideal demand system (PCAIDS) model, useful for merger simulations, which can be econometrically estimated using price data for two firms in a market. The model is then applied to a database of the Argentine gasoline market, and its results are compared to the ones obtained with other alternative specifications.

Suggested Citation

  • Germán Coloma, 2004. "Econometric Estimation of PCAIDS Models," CEMA Working Papers: Serie Documentos de Trabajo. 276, Universidad del CEMA.
    • Handle: RePEc:cem:doctra:276
    as

    Download full text from publisher

    File URL: https://www.ucema.edu.ar/publicaciones/download/documentos/276.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Roy J. Epstein & Dniel L. Rubinfeld, 2004. "Merger Simulation with Brand-Level Margin: Extending PCAIDS with Nests," Industrial Organization 0401003, University Library of Munich, Germany.
    2. Jerry Hausman & Gregory Leonard & J. Douglas Zona, 1994. "Competitive Analysis with Differentiated Products," Annals of Economics and Statistics, GENES, issue 34, pages 143-157.
    3. repec:adr:anecst:y:1994:i:34:p:06 is not listed on IDEAS
    4. Roy J. Epstein & Daniel L. Rubinfeld, 2002. "Merger Simulation: A Simplified Approach with New Applications," Industrial Organization 0201002, University Library of Munich, Germany.
    5. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-326, June.
    6. Epstein Roy J. & Rubinfeld Daniel L., 2004. "Merger Simulation with Brand-Level Margin Data: Extending PCAIDS with Nests," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 4(1), pages 1-28, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Gregory Swinand & Hugh Hennessy, 2014. "Estimating postal demand elasticities using the PCAIDS method," Chapters, in: Michael A. Crew & Timothy J. J. Brennan (ed.), The Role of the Postal and Delivery Sector in a Digital Age, chapter 5, pages 65-74, Edward Elgar Publishing.
    2. Oliver Budzinski & Isabel Ruhmer, 2010. "Merger Simulation In Competition Policy: A Survey," Journal of Competition Law and Economics, Oxford University Press, vol. 6(2), pages 277-319.
    3. Lundmark, Robert & Wårell, Linda, 2008. "Horizontal mergers in the iron ore industry--An application of PCAIDS," Resources Policy, Elsevier, vol. 33(3), pages 129-141, September.
    4. Chi Kong Chyong & David M Reiner & Dhruvak Aggarwal, 2023. "Market Power and Long-term Gas Contracts: The Case of Gazprom in Central and Eastern European Gas Markets," The Energy Journal, , vol. 44(1), pages 55-74, January.
    5. Gordon, Daniel V., 2018. "Country of origin growth modelling for imported salted & dried (Klippfisk) products to Brazil," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 31-43.

    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. Oliver Budzinski & Isabel Ruhmer, 2010. "Merger Simulation In Competition Policy: A Survey," Journal of Competition Law and Economics, Oxford University Press, vol. 6(2), pages 277-319.
    2. Lundmark, Robert & Wårell, Linda, 2008. "Horizontal mergers in the iron ore industry--An application of PCAIDS," Resources Policy, Elsevier, vol. 33(3), pages 129-141, September.
    3. Gordon, Daniel V., 2018. "Country of origin growth modelling for imported salted & dried (Klippfisk) products to Brazil," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 31-43.
    4. Kaplow, Louis & Shapiro, Carl, 2007. "Antitrust," Handbook of Law and Economics, in: A. Mitchell Polinsky & Steven Shavell (ed.), Handbook of Law and Economics, edition 1, volume 2, chapter 15, pages 1073-1225, Elsevier.
    5. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    6. Miller, Nathan H. & Remer, Marc & Ryan, Conor & Sheu, Gloria, 2017. "Upward pricing pressure as a predictor of merger price effects," International Journal of Industrial Organization, Elsevier, vol. 52(C), pages 216-247.
    7. Jerome Foncel & Marc Ivaldi & Jrisy Motis, 2008. "An Econometric Workbench for Comparing the Substantive and Dominance Tests in Horizontal Merger Analysis," Working Papers 0833, University of Crete, Department of Economics.
    8. Gregory Swinand & Hugh Hennessy, 2014. "Estimating postal demand elasticities using the PCAIDS method," Chapters, in: Michael A. Crew & Timothy J. J. Brennan (ed.), The Role of the Postal and Delivery Sector in a Digital Age, chapter 5, pages 65-74, Edward Elgar Publishing.
    9. David Weiskopf, 2000. "The Impact of Omitting Promotion Variables on Simulation Experiments," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 7(2), pages 159-166.
    10. Dongling Huang & Christian Rojas & Frank Bass, 2008. "What Happens When Demand Is Estimated With A Misspecified Model?," Journal of Industrial Economics, Wiley Blackwell, vol. 56(4), pages 809-839, December.
    11. Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-342, March.
    12. Yumin Li & Yan Jiang & Shiyuan Li, 2022. "Price and income elasticities of electricity in China: Estimation and policy implications," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S2), pages 76-90, November.
    13. Ryo Sakamoto & Kyle Stiegert, 2018. "Comparing competitive toughness to benchmark outcomes in retail oligopoly pricing," Agribusiness, John Wiley & Sons, Ltd., vol. 34(1), pages 44-60, December.
    14. van Damme, E.E.C. & Pinkse, J., 2005. "Merger Simulation Analysis : An Academic Perspective," Other publications TiSEM 1787127b-d84e-4ab6-b040-a, Tilburg University, School of Economics and Management.
    15. Nathan H. Miller & Gloria Sheu, 2021. "Quantitative Methods for Evaluating the Unilateral Effects of Mergers," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 58(1), pages 143-177, February.
    16. Keaton Miller & Boyoung Seo, 2021. "The Effect of Cannabis Legalization on Substance Demand and Tax Revenues," National Tax Journal, University of Chicago Press, vol. 74(1), pages 107-145.
    17. Affeldt, P. & Argentesi, E. & Filistrucchi, Lapo, 2021. "Estimating Demand with Multi-Homing in Two-Sided Markets," Other publications TiSEM 1317bf39-d02e-4f61-a34f-e, Tilburg University, School of Economics and Management.
    18. Mathiesen, Lars & Nilsen, Øivind Anti & Sørgard, Lars, 2011. "Merger simulations with observed diversion ratios," International Review of Law and Economics, Elsevier, vol. 31(2), pages 83-91, June.
    19. Victor Aguirregabiria & Margaret Slade, 2017. "Empirical models of firms and industries," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1445-1488, December.
    20. Fabrice Etilé & Sébastien Lecocq & Christine Boizot-Szantai, 2021. "Market heterogeneity and the distributional incidence of soft-drink taxes: evidence from France [Regressive sin taxes, with an application to the optimal soda tax]," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(4), pages 915-939.

    More about this item

    Keywords

    PCAIDS; demand estimation; merger simulations; Argentine gasoline market;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • L71 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Hydrocarbon Fuels

    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:cem:doctra:276. 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: Valeria Dowding (email available below). General contact details of provider: https://edirc.repec.org/data/cemaaar.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.