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Predicting Advertising Volumes Using Structural Time Series Models: A Case Study

Author

Listed:
  • Ralf Dewenter

    (Helmu-Schmidt-University Hamburg)

  • Ulrich Heimeshoff

    (Heinrich-Heine-University of Duesseldorf)

Abstract

Media platforms typically operate in a two-sided market, where advertising space serves as a major source of revenues. However, advertising volumes are highly volatile over time and characterized by cyclical behavior. Firms' marketing expenditures in general are far from stable. Due to planning of future issues as well as financial planning, platforms have to forecast the demand for advertising space in their future issues. We use structural time series analysis to predict advertising volumes and compare the results with simple autoregressive models.

Suggested Citation

  • Ralf Dewenter & Ulrich Heimeshoff, 2017. "Predicting Advertising Volumes Using Structural Time Series Models: A Case Study," Economics Bulletin, AccessEcon, vol. 37(3), pages 1644-1652.
    • Handle: RePEc:ebl:ecbull:eb-17-00140
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    References listed on IDEAS

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    Cited by:

    1. Yongyi Zhou & Yulin Zhang & Mark Goh, 2021. "Choice of pricing and advertising schemes for a two‐sided platform," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1885, October.

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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • L8 - Industrial Organization - - Industry Studies: Services

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