Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning

Published Online:https://doi.org/10.1287/mnsc.2018.3197

References

  • Ackerberg D (2003) Advertising, learning, and consumer choice in experience good markets: An empirical examination. Internat. Econom. Rev. 44(3):1007–1040.CrossrefGoogle Scholar
  • Aghion P, Bolton P, Harris C, Julien B (1991) Optimal learning by experimentation. Rev. Econom. Stud. 58(4):621–654.CrossrefGoogle Scholar
  • Araman V, Caldentey R (2009) Dynamic pricing for nonperishable products with demand learning. Oper. Res. 57(5):1169–1188.LinkGoogle Scholar
  • Aviv Y, Pazgal A (2005) Dynamic pricing of short life-cycle products through active learning. Working paper, Washington University, St. Louis.Google Scholar
  • Benítez-Silva H, Hall G, Hitsch G, Pauletto G, Brook S, Rust J (2000) A comparison of discrete and parametric approximation methods for continuous-state dynamic programming problems. Working paper, Yale University, New Haven, CT.Google Scholar
  • Benkard L (2004) A dynamic analysis of the market for wide-bodied commercial aircraft. Rev. Econom. Stud. 71(3):581–611.CrossrefGoogle Scholar
  • Berry S, Levinsohn J, Pakes A (1995) Automobile prices in market equilibrium. Econometrica 63(4):841–890.CrossrefGoogle Scholar
  • Blackwell D (1953) Equivalent comparison of experiments. Ann. Math. Statist. 24(2):265–272.CrossrefGoogle Scholar
  • CarMax, Inc. (2011) Car Max, Inc. Annual Report. Fiscal Year 2011. Accessed July 24, 2019, http://s21.q4cdn.com/483767183/files/doc_financials/Annual%20Report/CarMax_11AR_v001_s1m120.pdf.Google Scholar
  • Ching AT (2010a) Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration. Internat. J. Indust. Organ. 28(6):619–638.CrossrefGoogle Scholar
  • Ching AT (2010b) A dynamic oligopoly structural model for the prescription drug market after patent expiration. Internat. Econom. Rev. 51(4):1175–1207.CrossrefGoogle Scholar
  • Ching AT, Ishihara M (2010) The effects of detailing on prescribing decisions under quality uncertainty. Quant. Marketing Econom. 8(2):123–165.CrossrefGoogle Scholar
  • Ching AT, Erdem T, Keane MP (2013) Invited paper-learning models: An assessment of progress, challenges, and new developments. Marketing Sci. 32(6):913–938.LinkGoogle Scholar
  • Ching AT, Erdem T, Keane MP (2017) Empirical models of learning dynamics: A survey of recent developments. Wierenga B, van der Lans R, eds. Handbook of Marketing Decision Models, International Series in Operations Research & Management Science, vol. 254. (Springer, Cham, Switzerland), 223–257.CrossrefGoogle Scholar
  • Crawford G, Shum M (2005) Uncertainty and learning in pharmaceutical demand. Econometrica 73(4):1137–1173.CrossrefGoogle Scholar
  • Easley D, Kiefer NM (1988) Controlling a stochastic process with unknown parameter. Econometrica 56(5):1045–1064.CrossrefGoogle Scholar
  • Einav L, Farronato C, Levin J, Sundaresan N (2018) Auctions versus posted prices in online markets. J. Political Econom. 126(1):178–215.CrossrefGoogle Scholar
  • Erdem T, Keane M (1996) Decision-making under uncertainty: Capturing dynamic brand choice processes in turbulent consumer goods markets. Marketing Sci. 15(1):1–20.LinkGoogle Scholar
  • Erdem T, Keane M, Sun B (2008) A dynamic model of brand choice when price and advertising signal product quality. Marketing Sci. 27(6):1111–1125.LinkGoogle Scholar
  • Erdem T, Keane MP, Sun B (1999) Missing price and coupon availability data in scanner panels: Correcting for the self-selection bias in choice model parameters. J. Econometrics 89(1–2):177–196.CrossrefGoogle Scholar
  • Fernandez-Villaverde J, Rubio-Ramirez J (2007) Estimating macroeconomic models: A likelihood approach. Rev. Econom. Stud. 74(4):1059–1087.CrossrefGoogle Scholar
  • Flury T, Shephard N (2011) Bayesian inference based only on simulated likelihood: Particle filter analysis of dynamic economic models. Econom. Theory 27(5):933–956.CrossrefGoogle Scholar
  • Gallant AR, Hong H, Khwaja A (2018) A Bayesian approach to estimation of dynamic models with small and large number of heterogeneous players and latent serially correlated states. J. Econom. 203(1):19–32.CrossrefGoogle Scholar
  • Grossman SJ, Kihlstrom RE, Mirman LJ (1977) A Bayesian approach to the production of information and learning by doing. Rev. Econom. Stud. 44(3):533–547.CrossrefGoogle Scholar
  • Handel BR, Misra K (2015) Robust new product pricing. Marketing Sci. 34(6):864–881.LinkGoogle Scholar
  • Hitsch GJ (2006) An empirical model of optimal dynamic product launch and exit under demand uncertainty. Marketing Sci. 25(1):25–50.LinkGoogle Scholar
  • Ishihara M, Ching AT (2019) Dynamic demand for new and used durable goods without physical depreciation: The case of Japanese video games. Marketing Sci. 38(3):392–416.LinkGoogle Scholar
  • Larsen B (2014) The efficiency of real-world bargaining: Evidence from wholesale used-auto auctions. Working paper, Stanford University, Stanford, CA.Google Scholar
  • Lewis G (2011) Asymmetric information, adverse selection and online disclosure: The case of ebay motors. Amer. Econom. Rev. 101(4):1535–1546.CrossrefGoogle Scholar
  • Ligon A (2002) CarMax CEO interview, interview by Mark Haines. CNBC/Dow Jones Business Video (October 1).Google Scholar
  • Mason R, Välimäki J (2011) Learning about the arrival of sales. J. Econom. Theory 146(4):1699–1711.CrossrefGoogle Scholar
  • Merlo A, Ortalo-Magne F (2004) Bargaining over residential real estate: Evidence from England. J. Urban Econom. 56(2):192–216.CrossrefGoogle Scholar
  • Merlo A, Ortalo-Magné F, Rust J (2015) The home selling problem: Theory and evidence. Internat. Econom. Rev. 56(2):457–484.CrossrefGoogle Scholar
  • Mirman L, Samuelson L, Urbano A (1993) Monopoly experimentation. Internat. Econom. Rev. 34(3):549–563.CrossrefGoogle Scholar
  • Nair H (2007) Intertemporal price discrimination with forward-looking consumers: Application to the us market for console video-games. Quant. Marketing Econom. 5(3):239–292.CrossrefGoogle Scholar
  • Newberry PW (2016) An empirical study of observational learning. RAND J. Econom. 47(2):394–432.CrossrefGoogle Scholar
  • Petrin A, Train K (2010) A control function approach to endogeneity in consumer choice models. J. Marketing Res. 47(1):3–13.CrossrefGoogle Scholar
  • Riley J, Zeckhauser R (1983) Optimal selling strategies: When to haggle, when to hold firm. Quart. J. Econom. 98(2):267–289.CrossrefGoogle Scholar
  • Rothschild M (1974) A two-armed bandit theory of market pricing. J. Econom. Theory 9(2):185–202.CrossrefGoogle Scholar
  • Rubin D (1988) Using the SIR algorithm to simulate posterior distributions. Bernardo J, DeGroot M, Lindley D, Smith A eds. Bayesian Statistics 3 (Oxford University Press, Oxford, UK) 395–402.Google Scholar
  • Rust J (1987) Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher. Econometrica 55(5):999–1033.CrossrefGoogle Scholar
  • Tadelis S, Zettelmeyer F (2015) Information disclosure as a matching mechanism: Theory and evidence from a field experiment. Amer. Econom. Rev. 105(2):886–905.CrossrefGoogle Scholar
  • Taylor C (1999) Time-on-the-market as a sign of quality. Rev. Econom. Stud. 66(3):555–578.CrossrefGoogle Scholar
  • Trefler D (1993) The ignorant monopolist: Optimal learning with endogenous information. Internat. Econom. Rev. 34(3):565–581.CrossrefGoogle Scholar
  • Xu X, Hopp W (2005) Dynamic pricing and inventory control: The value of demand learning. Working paper, Northwestern University, Evanston, IL.Google Scholar
  • Zhang J (2010) The sound of silence: Observational learning in the U.S. kidney market. Marketing Sci. 29(2):315–335.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.