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Failure to Launch in Two-Sided Markets: A Study of the U.S. Video Game Market

  • Zhou, Yiyi

In the dynamic two-sided market environment, overpricing one side of the market not only discourages demand on that side but also discourages participation on the other side. Over time, this process can lead to a death spiral. This paper develops a dynamic structural model of the video game market to study launch failures in two-sided markets. The paper models consumers’ purchase decisions for hardware platforms and affiliated software products and software firms’ entry and pricing decisions. This paper also develops a Bayesian Markov Chain Monte Carlo approach to estimate dynamic structural models. The results of the counterfactual simulations show that a failed platform could have survived if it had lowered its hardware prices and that it could not have walked out of the death spiral if it had subsidized software entry.

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File URL: http://mpra.ub.uni-muenchen.de/42002/1/MPRA_paper_42002.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 42002.

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Date of creation: 16 Oct 2012
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Handle: RePEc:pra:mprapa:42002
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  1. J. Levin & P. Bajari, 2004. "Estimating Dynamic Models of Imperfect Competition," 2004 Meeting Papers 579, Society for Economic Dynamics.
  2. Yingyao Hu & Matthew Shum, 2008. "Nonparametric identification of dynamic models with unobserved state variables," CeMMAP working papers CWP13/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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  4. Marc Rysman, 2009. "The Economics of Two-Sided Markets," Journal of Economic Perspectives, American Economic Association, vol. 23(3), pages 125-43, Summer.
  5. Harikesh Nair, 2007. "Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games," Quantitative Marketing and Economics, Springer, vol. 5(3), pages 239-292, September.
  6. Jiang, Renna & Manchanda, Puneet & Rossi, Peter E., 2009. "Bayesian analysis of random coefficient logit models using aggregate data," Journal of Econometrics, Elsevier, vol. 149(2), pages 136-148, April.
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  8. Evans David S. & Schmalensee Richard, 2010. "Failure to Launch: Critical Mass in Platform Businesses," Review of Network Economics, De Gruyter, vol. 9(4), pages 1-28, December.
  9. Michael P. Keane & Kenneth I. Wolpin, 1994. "The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence," Staff Report 181, Federal Reserve Bank of Minneapolis.
  10. Ronald L. Goettler & Brett R. Gordon, 2011. "Does AMD Spur Intel to Innovate More?," Journal of Political Economy, University of Chicago Press, vol. 119(6), pages 1141 - 1200.
  11. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics, Springer, vol. 10(2), pages 151-196, June.
  12. Caillaud, Bernard & Jullien, Bruno, 2003. " Chicken & Egg: Competition among Intermediation Service Providers," RAND Journal of Economics, The RAND Corporation, vol. 34(2), pages 309-28, Summer.
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  14. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
  15. Ulrich Doraszelski & Mark Satterthwaite, 2010. "Computable Markov-perfect industry dynamics," RAND Journal of Economics, RAND Corporation, vol. 41(2), pages 215-243.
  16. repec:rje:randje:v:37:y:2006:3:p:645-667 is not listed on IDEAS
  17. Ericson, Richard & Pakes, Ariel, 1995. "Markov-Perfect Industry Dynamics: A Framework for Empirical Work," Review of Economic Studies, Wiley Blackwell, vol. 62(1), pages 53-82, January.
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