IDEAS home Printed from https://ideas.repec.org/p/tor/tecipa/tecipa-275.html
   My bibliography  Save this paper

An Estimable Dynamic Model of Entry, Exit and Growth in Oligopoly Retail Markets

Author

Listed:
  • Victor Aguirregabiria
  • Pedro Mira
  • Hernan Roman

Abstract

This paper presents an estimable dynamic structural model of an oligopoly retail industry. The model can be estimated using panel data of local retail markets with information on new entries, exits and the size and growth of incumbent firms. In our model, retail firms are vertically and horizontally differentiated, compete in prices, make investments to improve the quality of their businesses, and decide to exit or to continue in the market. The model extends in two important ways the entry-exit model estimated in Aguirregabiria and Mira (2007). First, it includes firm size and growth as endogenous variables. And second, the empirical model has two sources of permanent unobserved heterogeneity: local-market heterogeneity and firm heterogeneity. This allows the researcher to control for potentially important sources of bias when using firm panel data with many local markets and several time periods.

Suggested Citation

  • Victor Aguirregabiria & Pedro Mira & Hernan Roman, 2007. "An Estimable Dynamic Model of Entry, Exit and Growth in Oligopoly Retail Markets," Working Papers tecipa-275, University of Toronto, Department of Economics.
    • Handle: RePEc:tor:tecipa:tecipa-275
    as

    Download full text from publisher

    File URL: https://www.economics.utoronto.ca/public/workingPapers/tecipa-275-1.pdf
    File Function: Main Text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2007. "Estimating Dynamic Models of Imperfect Competition," Econometrica, Econometric Society, vol. 75(5), pages 1331-1370, September.
    2. Victor Aguirregabiria & Pedro Mira, 2007. "Sequential Estimation of Dynamic Discrete Games," Econometrica, Econometric Society, vol. 75(1), pages 1-53, January.
    3. Ariel Pakes & Michael Ostrovsky & Steven Berry, 2007. "Simple estimators for the parameters of discrete dynamic games (with entry/exit examples)," RAND Journal of Economics, RAND Corporation, vol. 38(2), pages 373-399, June.
    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. Florin Maican & Matilda Orth, 2017. "Productivity Dynamics and the Role of ‘Big-Box’ Entrants in Retailing," Journal of Industrial Economics, Wiley Blackwell, vol. 65(2), pages 397-438, June.
    2. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    3. Lauren Chenarides & Metin Çakır & Timothy J. Richards, 2024. "Dynamic model of entry: Dollar stores," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(2), pages 852-882, March.
    4. Jonathan Haskel & Raffaella Sadun, 2012. "Regulation and UK Retailing Productivity: Evidence from Microdata," Economica, London School of Economics and Political Science, vol. 79(315), pages 425-448, July.
    5. Joao Macieira, 2010. "Oblivious Equilibrium in Dynamic Discrete Games," 2010 Meeting Papers 680, Society for Economic Dynamics.
    6. Yingyao Hu & Yi Xin, 2019. "Identi?cation and estimation of dynamic structural models with unobserved choices," CeMMAP working papers CWP35/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Yu Zheng & Juan Pantano, 2012. "Using Subjective Expectations Data to Allow for Unobserved Heterogeneity in Hotz-Miller Estimation Strategies," 2012 Meeting Papers 940, Society for Economic Dynamics.
    8. Aguirregabiria, Victor & Nevo, Aviv, 2010. "Recent developments in empirical IO: dynamic demand and dynamic games," MPRA Paper 27814, University Library of Munich, Germany.
    9. Yanyou Chen & Daniel Yi Xu, 2023. "A Structural Empirical Model of R&D, Firm Heterogeneity, and Industry Evolution," Journal of Industrial Economics, Wiley Blackwell, vol. 71(2), pages 323-353, June.
    10. Timothy J. Richards & Simba Pasirayi, 2024. "Food retailing in developing economies: Review of empirical insights and new directions," Agribusiness, John Wiley & Sons, Ltd., vol. 40(4), pages 926-949, October.
    11. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2015. "Identification of Counterfactuals in Dynamic Discrete Choice Models," NBER Working Papers 21527, National Bureau of Economic Research, Inc.
    12. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2015. "Identification of Counterfactuals and Payoffs in Dynamic Discrete Choice with an Application to Land Use," Working Papers tecipa-546, University of Toronto, Department of Economics.
    13. Richards, Timothy J. & Chenarides, Lauren & Çakir, Metin, 2022. "Dollar Store Entry," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322100, Agricultural and Applied Economics Association.
    14. Timothy J. Richards & Bradley J. Rickard, 2021. "Dynamic model of beer pricing and buyouts," Agribusiness, John Wiley & Sons, Ltd., vol. 37(4), pages 685-712, October.
    15. Sears, Louis S. & Lin Lawell, C.-Y. Cynthia & Walter, M. Todd, 2020. "Groundwater Under Open Access: A Structural Model of the Dynamic Common Pool Extraction Game," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304276, Agricultural and Applied Economics Association.

    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. Paul Ellickson & Sanjog Misra, 2012. "Enriching interactions: Incorporating outcome data into static discrete games," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 1-26, March.
    2. Mitsukuni Nishida, 2015. "Estimating a Model of Strategic Network Choice: The Convenience-Store Industry in Okinawa," Marketing Science, INFORMS, vol. 34(1), pages 20-38, January.
    3. Joao Macieira, 2010. "Oblivious Equilibrium in Dynamic Discrete Games," 2010 Meeting Papers 680, Society for Economic Dynamics.
    4. Andrew Sweeting, 2007. "Dynamic Product Repositioning in Differentiated Product Markets: The Case of Format Switching in the Commercial Radio Industry," NBER Working Papers 13522, National Bureau of Economic Research, Inc.
    5. Jayakumar Subramanian & Amit Sinha & Aditya Mahajan, 2023. "Robustness and Sample Complexity of Model-Based MARL for General-Sum Markov Games," Dynamic Games and Applications, Springer, vol. 13(1), pages 56-88, March.
    6. Maruyama, Shiko, 2014. "Estimation of finite sequential games," Journal of Econometrics, Elsevier, vol. 178(2), pages 716-726.
    7. Hanming Fang & Yang Wang, 2015. "Estimating Dynamic Discrete Choice Models With Hyperbolic Discounting, With An Application To Mammography Decisions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(2), pages 565-596, May.
    8. Hiroyuki Kasahara & Katsumi Shimotsu, 2012. "Sequential Estimation of Structural Models With a Fixed Point Constraint," Econometrica, Econometric Society, vol. 80(5), pages 2303-2319, September.
    9. Arcidiacono, Peter & Miller, Robert A., 2020. "Identifying dynamic discrete choice models off short panels," Journal of Econometrics, Elsevier, vol. 215(2), pages 473-485.
    10. Johannes Van Biesebroeck & Aamir Hashmi, 2007. "Market Structure and Innovation: A Dynamic Analysis of the Global Automobile Industry," 2007 Meeting Papers 362, Society for Economic Dynamics.
    11. Áureo de Paula, 2013. "Econometric Analysis of Games with Multiple Equilibria," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 107-131, May.
    12. Kasahara, Hiroyuki & Shimotsu, Katsumi, 2008. "Pseudo-likelihood estimation and bootstrap inference for structural discrete Markov decision models," Journal of Econometrics, Elsevier, vol. 146(1), pages 92-106, September.
    13. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    14. Komarova, Tatiana & Sanches, Fábio Adriano & Silva Junior, Daniel & Srisuma, Sorawoot, 2018. "Joint analysis of the discount factor and payoff parameters in dynamic discrete choice games," LSE Research Online Documents on Economics 86858, London School of Economics and Political Science, LSE Library.
    15. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    16. Xiao, Mo & Orazem, Peter F., 2011. "Does the fourth entrant make any difference?: Entry and competition in the early U.S. broadband market," International Journal of Industrial Organization, Elsevier, vol. 29(5), pages 547-561, September.
    17. Liu, An-Hsiang & Siebert, Ralph B., 2022. "The competitive effects of declining entry costs over time: Evidence from the static random access memory market," International Journal of Industrial Organization, Elsevier, vol. 80(C).
    18. Sears, Louis S. & Lin Lawell, C.-Y. Cynthia & Walter, M. Todd, 2020. "Groundwater Under Open Access: A Structural Model of the Dynamic Common Pool Extraction Game," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304276, Agricultural and Applied Economics Association.
    19. Ulrich Doraszelski & Mark Satterthwaite, 2010. "Computable Markov‐perfect industry dynamics," RAND Journal of Economics, RAND Corporation, vol. 41(2), pages 215-243, June.
    20. Sumon Datta & K. Sudhir, 2012. "Does Reducing Spatial Differentiation Increase Product Differentiation" Effects of Zoning on Retail Entry and Format Variety," Cowles Foundation Discussion Papers 1851, Cowles Foundation for Research in Economics, Yale University, revised Sep 2012.

    More about this item

    Keywords

    Industry dynamics; Oligopoly retail markets; Structural estimation;
    All these keywords.

    JEL classification:

    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:tor:tecipa:tecipa-275. 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: RePEc Maintainer (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.