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Challenges in Statistically Rejecting the Perfect Competition Hypothesis Using Imperfect Competition Data

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  • Yuri Matsumura
  • Suguru Otani

Abstract

We theoretically prove why statistically rejecting the null hypothesis of perfect competition is challenging, known as a common problem in the literature. We also assess the finite sample performance of the conduct parameter test in homogeneous goods markets, showing that statistical power increases with the number of markets, a larger conduct parameter, and a stronger demand rotation instrument. However, even with a moderate number of markets and five firms, rejecting the null hypothesis of perfect competition remains difficult, irrespective of instrument strength or the use of optimal instruments. Our findings suggest that empirical results failing to reject perfect competition are due to the limited number of markets rather than methodological shortcomings.

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  • Yuri Matsumura & Suguru Otani, 2023. "Challenges in Statistically Rejecting the Perfect Competition Hypothesis Using Imperfect Competition Data," Papers 2310.04576, arXiv.org, revised Aug 2024.
    • Handle: RePEc:arx:papers:2310.04576
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    3. Luis Orea & Jevgenijs Steinbuks, 2018. "Estimating Market Power In Homogenous Product Markets Using A Composed Error Model: Application To The California Electricity Market," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 1296-1321, April.
    4. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
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