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README_file.txt text/plain 1020 bytes 03/31/2023 04:44:PM
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figures2_and_3_AEA_PP.R text/x-rsrc 7.6 KB 03/05/2023 08:51:AM
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gammas_US_France_Thailand.RData application/x-rlang-transport 381 bytes 03/05/2023 08:51:AM text/plain 7.7 KB 03/05/2023 08:24:AM
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Project Citation: 

Breitmar, Nils, Harding, Matthew, and Lamarche, Carlos. Replication Code for Using Grouped Data to Estimate Revenue Heterogeneity in Online Advertising Markets. Nashville, TN: American Economic Association [publisher], 2023. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-04-28.

Project Description

Summary:  View help for Summary In this paper, we showed that a publisher of online ads could estimate informative models, such as revenue models, from a position of limited visibility into the auction process conducted by intermediary ad platforms, even though these platforms provide only grouped data to the publisher. We validate our results using singleton data available to the publishers on auction data from unique customers in specific location-times. We also address the high-dimensional nature of the data and allow for time-varying individual heterogeneity through interactive effects. Our empirical results show that ad networks impact revenue heterogeneity.
Funding Sources:  View help for Funding Sources None

Scope of Project

Subject Terms:  View help for Subject Terms online auctions; revenue heterogeneity; Big Data
JEL Classification:  View help for JEL Classification
      C13 Estimation: General
      C33 Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
Geographic Coverage:  View help for Geographic Coverage Global
Time Period(s):  View help for Time Period(s) 7/1/2022 – 7/31/2022 (Four weeks in July 2022)
Collection Date(s):  View help for Collection Date(s) 7/1/2022 – 7/31/2022 (July 2022)
Universe:  View help for Universe Ad impressions
Data Type(s):  View help for Data Type(s) program source code

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