Replication Code for Using Grouped Data to Estimate Revenue Heterogeneity in Online Advertising Markets

Principal Investigator(s): View help for Principal Investigator(s) Nils Breitmar, Columbia University; Matthew Harding, University of California-Irvine; Carlos Lamarche, University of Kentucky

Version: View help for Version V1

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README_file.txt	text/plain	1020 bytes	03/31/2023 04:44:PM
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main_results_AEA_PP.log	text/x-log	25 KB	02/26/2023 02:11:PM

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. https://doi.org/10.3886/E187902V1

Project Description

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: None

Scope of Project

Subject Terms: online auctions; revenue heterogeneity; Big Data

JEL Classification:
C13 Estimation: General
C33 Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models

Geographic Coverage: Global

Time Period(s): 7/1/2022 – 7/31/2022 (Four weeks in July 2022)

Collection Date(s): 7/1/2022 – 7/31/2022 (July 2022)

Universe: Ad impressions

Data Type(s): program source code

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Published Versions

V1 [2023-04-28]

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