Identification and Estimation of Online Price Competition With an Unknown Number of Firms
This paper considers identification and estimation of a general model for online price competition. We show that when the number of competing firms is unknown the underlying parameters of the model can still be identified and estimated employing recently developed results on measurement errors. We illustrate our methodology using UK data for personal digital assistants and employ the estimates to simulate competitive effects. Our results reveal that heightened competition has differential effects on the prices paid by different consumer segments.
© 2015 John Wiley & Sons, Ltd. Issue online: 2 February 2017. Version of record online: 9 November 2015. Manuscript Accepted: 7 August 2015. Manuscript Revised: 6 August 2015. Manuscript Received: 4 December 2012. We are grateful to the co-editor Thierry Magnac and three anonymous referees for their exceptionally helpful comments. This research began while Baye was serving as the Director of the Bureau of Economics at the Federal Trade Commission. We thank his former colleagues there, especially Dan O'Brien and Dan Hosken, for helpful discussions. We also thank seminar participants at Northwestern University and the University of Connecticut (Department of Agricultural and Resource Economics) for comments on a preliminary draft. Morgan thanks the National Science Foundation for financial support.
Submitted - SSRN-id2752576.pdf
Supplemental Material - jae2492-sup-0001-Supplementary1.pdf