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Forensic Econometrics: Demand Estimation When Data are Missing

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  • Julian Hidalgo

    (KU Leuven)

  • Michelle Sovinsky

    (University of Mannheim and CEPR)

Abstract

Often empirical researchers face many data constraints when estimating models of demand. These constraints can sometimes prevent adequate evaluation of policies. In this article, we discuss two such missing data problems that arise frequently: missing data on prices and missing information on the size of the potential market. We present some ways to overcome these limitations in the context of two recent research projects. Jacobi and Sovinsky (2018), which addresses how to incorporate unobserved price heterogeneity, and Hidalgo and Sovinsky (2018), which focuses on how to use modelling techniques to estimate missing market size. Our aim is to provide a starting point for thinking about ways to overcome common data issues.

Suggested Citation

  • Julian Hidalgo & Michelle Sovinsky, 2019. "Forensic Econometrics: Demand Estimation When Data are Missing," The Japanese Economic Review, Springer, vol. 70(3), pages 403-410, September.
    • Handle: RePEc:spr:jecrev:v:70:y:2019:i:3:d:10.1111_jere.12242
    DOI: 10.1111/jere.12242
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    References listed on IDEAS

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    3. Liana Jacobi & Michelle Sovinsky, 2016. "Marijuana on Main Street? Estimating Demand in Markets with Limited Access," American Economic Review, American Economic Association, vol. 106(8), pages 2009-2045, August.
    4. Matthias Parey & Imran Rasul, 2021. "Measuring the Market Size for Cannabis: A New Approach Using Forensic Economics," Economica, London School of Economics and Political Science, vol. 88(350), pages 297-338, April.
    5. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer.
    6. Michelle Sovinsky Goeree, 2008. "Limited Information and Advertising in the U.S. Personal Computer Industry," Econometrica, Econometric Society, vol. 76(5), pages 1017-1074, September.
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