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Forecasting the Penetration of a New Product--A Bayesian Approach

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  • Pammer, Scott E
  • Fong, Duncan K H
  • Arnold, Steven F

Abstract

We adopt a Bayesian approach to forecast the penetration of a new product into a market. We incorporate prior information from an existing product and/or management judgments into the data analysis. The penetration curve is assumed to be a nondecreasing function of time and may be under shape constraints. Markov-chain Monte Carlo methods are proposed and used to compute the Bayesian forecasts. An example on forecasting the penetration of color television using the information from black-and-white television is provided. The models considered can also be used to address the general bioassay and reliability stress-testing problems.

Suggested Citation

  • Pammer, Scott E & Fong, Duncan K H & Arnold, Steven F, 2000. "Forecasting the Penetration of a New Product--A Bayesian Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 428-435, October.
    • Handle: RePEc:bes:jnlbes:v:18:y:2000:i:4:p:428-35
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    Cited by:

    1. E Stavrulaki & D K H Fong & D K J Lin, 2003. "Two-resource stochastic capacity planning employing a Bayesian methodology," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(11), pages 1198-1208, November.
    2. Gary J. Summers, 2021. "Friction and Decision Rules in Portfolio Decision Analysis," Decision Analysis, INFORMS, vol. 18(2), pages 101-120, June.

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