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Modeling Oligopolistic Price Adjustment in Micro Level Panel Data


  • Jürgen Bracht
  • Saul Lach
  • Eyal Winter


Consumer prices in many markets are persistently dispersed both across retail outlets and over time. While the cross sectional distribution of prices is stable, individual stores change their position in the distribution over time. It is a challenge to model oligopolistic price adjustment to capture these features of consumer markets. In belief based models of price adjustment stores react to expected profits. The expectations are based on the observed vector of market prices in the previous periods. In a reinforcement model of price adjustment, if a strategy has proven fruitful in the past, it is apt to be the strategy relied upon at the present. We collect price data on a homogeneous consumer product in Israel. We estimate the structural parameter of the models. We find that the reinforcement model describes the data better than the belief based models. ZUSAMMENFASSUNG - ( Modeling Oligopolistic Price Adjustment in Micro Level Panel Data) Preise für viele Konsumgüter sind weit verteilt. Dies gilt sowohl für die Verteilung über die Zeit als auch für die Verteilung zwischen den Verkaufsstellen. Während die Querschnittsverteilung der Preise stabil ist, wechseln die einzelnen Verkaufsstellen ihre Position in der Verteilung über die Zeit. Es stellt eine Herausforderung dar, diese Merkmale der Märkte für Konsumgüter zu modellieren. Im Vermutungslernen bilden die Verkaufstätten Erwartungen über das zukünftige Preissetzungsverhalten der Konkurrenz. Die Erwartungen basieren auf dem vorherigen Entscheidungsverhalten der Konkurrenz. Im Bekräftigungslernen werden erfolgreiche Strategien gerne wiederholt. Preisdaten eines homogenen Gutes in Israel werden erhoben. Die strukturellen Parameter der Modelle werden geschätzt. Bekräftigungslernen beschreibt das tatsächliche Entscheidungsverhalten besser als Vermutungslernen.

Suggested Citation

  • Jürgen Bracht & Saul Lach & Eyal Winter, 2002. "Modeling Oligopolistic Price Adjustment in Micro Level Panel Data," CIG Working Papers FS IV 02-24, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
  • Handle: RePEc:wzb:wzebiv:fsiv02-24

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    References listed on IDEAS

    1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    2. Saul Lach, 2002. "Existence And Persistence Of Price Dispersion: An Empirical Analysis," The Review of Economics and Statistics, MIT Press, vol. 84(3), pages 433-444, August.
    3. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
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    Experiments; Information; Learning;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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