Template-Type: ReDIF-Paper 1.0
Title: Forensic Econometrics: Demand Estimation When Data Are Missing
Author-Name: Julian Hidalgo
Author-Email: 
Author-Name: Michelle Sovinsky
Author-Email: 
Classification-JEL: 
Keywords:

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.

Note: 
Length: 13
Creation-Date: 2018-11
Revision-Date: 
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp058
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2018_058