Identification and Estimation of Online Price Competition with an Unknown Number of Firms

Yonghong An, Michael Baye, Yingyao Hu, John Morgan and Matt Shum
Additional contact information
Yingyao Hu: Johns Hopkins University
Matt Shum: Caltech

No 2010-17, Working Papers from Indiana University, Kelley School of Business, Department of Business Economics and Public Policy

Abstract: 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 error. With the estimates of model parameters, we are able to analyze the competitive effects of online competition when the number of firms changes. 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.

Keywords: E-Retail Markets; Nonparametric Identification; Structural Estimation (search for similar items in EconPapers)
JEL-codes: L0 (search for similar items in EconPapers)
Date: 2010-07, Revised 2012-11
New Economics Papers: this item is included in nep-bec, nep-com, nep-ict and nep-ind
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Related works:
Journal Article: Identification and Estimation of Online Price Competition With an Unknown Number of Firms (2017)
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