• Graduate Program
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • Summer School
      • Deep Learning
      • Economics of Blockchain and Digital Currencies
      • Foundations of Machine Learning with Applications in Python
      • Marketing Research with Purpose
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Tinbergen Institute Summer School Program
    • Annual Tinbergen Institute Conference archive
  • News
  • Summer School
    • Deep Learning
    • Economics of Blockchain and Digital Currencies
    • Foundations of Machine Learning with Applications in Python
    • Marketing Research with Purpose
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
  • Alumni

Franses, P.H. (2025). Adstock revisited Applied Economics, 57(8):882--886.

  • Journal
    Applied Economics
View publication

The standard Adstock regression is problematic for at least two reasons. Statistical inference is cumbersome and there is a chance of a spurious relation between sales and advertising. This paper shows that reformulating the equation solves both problems, and the result is that a proper model for Adstock is an unrestricted Koyck model. Maximum Likelihood estimation gives the parameter estimates. An illustration to the illustrious monthly Lydia Pinkham data shows the merits of the method.