Forecasting from others’ experience: Bayesian estimation of the generalized Bass model

Andrés Ramírez-Hassan and Santiago Montoya-Blandón
Authors registered in the RePEc Author Service: Andrés Ramírez Hassan ()

International Journal of Forecasting, 2020, vol. 36, issue 2, 442-465

Abstract: We propose a Bayesian estimation procedure for the generalized Bass model that is used in product diffusion models. Our method forecasts product sales early based on previous similar markets; that is, we obtain pre-launch forecasts by analogy. We compare our forecasting proposal to traditional estimation approaches, and alternative new product diffusion specifications. We perform several simulation exercises, and use our method to forecast the sales of room air conditioners, BlackBerry handheld devices, and compressed natural gas. The results show that our Bayesian proposal provides better predictive performances than competing alternatives when little or no historical data are available, which is when sales projections are the most useful.

Keywords: Bayesian estimation; Diffusion; Forecast by analogy; Generalized Bass model; Pre-launch forecast (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:442-465

DOI: 10.1016/j.ijforecast.2019.05.016

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