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Forensic Econometrics: Demand Estimation When Data Are Missing

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  • Julian Hidalgo
  • Michelle Sovinsky

Abstract

Often empirical researchers face many data constraints when estimating models of de- mand. 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. Liana and Sovin- sky (2018) which addresses how to incorporate unobserved price heterogeneity and Hidalgo and Sovinsky (2018) which focuses on how to use modeling techniques to estimate missing market size. Our aim is to provide a starting point for thinking about ways to overcome common data issues.

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  • Julian Hidalgo & Michelle Sovinsky, 2018. "Forensic Econometrics: Demand Estimation When Data Are Missing," CRC TR 224 Discussion Paper Series crctr224_2018_058, University of Bonn and University of Mannheim, Germany.
    • Handle: RePEc:bon:boncrc:crctr224_2018_058
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