Bayesian Estimation of a Dynamic Model of Two-Sided Markets: Application to the U.S. Video Game Industry

36 Pages Posted: 19 Oct 2012 Last revised: 30 Dec 2017

Date Written: April 1, 2016

Abstract

This paper develops and estimates a structural model of two-sided markets with durable platform intermediaries and affiliated products. It models buyers’ purchase decisions of plat- forms and affiliated products and sellers’ decisions of price-setting and entry, accounting for the dynamic interaction between the two distinct groups of platform participants. To estimate the proposed model, this paper develops a Bayesian Markov Chain Monte Carlo estimation approach that incorporates non-parametric approximation and interpolation methods. The proposed model and estimation method are applied to the 32/64-bit generation of U.S. video game industry. The results of counterfactual experiments show that the dynamic behavior of platform participants has significant impacts on the platform adoption and the affiliated product market, and that a failed platform could have survived if it had priced the two sides properly in a dynamic two-sided market environment.

Keywords: Two-Sided Market, Indirect Network Effect, Bayesian Markov Chain Monte Carlo (MCMC) Estimation, Video Game Market

JEL Classification: C11, D12, D22, D43, L11, L86

Suggested Citation

Zhou, Yiyi, Bayesian Estimation of a Dynamic Model of Two-Sided Markets: Application to the U.S. Video Game Industry (April 1, 2016). Available at SSRN: https://ssrn.com/abstract=2163948 or http://dx.doi.org/10.2139/ssrn.2163948

Yiyi Zhou (Contact Author)

Stony Brook University ( email )

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