Demand forecasting for multigenerational products combining discrete choice and dynamics of diffusion under technological trajectories

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The discrete choice model generally captures consumers' valuation of the product's quality within the framework of a cross-sectional analysis, while the diffusion model captures the dynamics of demand within the framework of a time-series analysis. We propose an adjusted discrete choice model that incorporates the choice behavior of the consumer into the dynamics of product diffusion. In addition, a new estimation structure is proposed, within the framework of the time-series analysis, which enables the estimation of the discrete choice model on market-level data to be performed in such a way as to avoid the problem of price endogeneity and to obtain greater flexibility in forecasting demand. As an empirical application, the suggested model is applied to the case of the worldwide DRAM (dynamic random access memory) market. In forecasting future demand of DRAM generations, we integrate Moore's law and learning by doing to reflect the future technological trajectories of DRAM innovations, as well as consumers' consumption trends to reflect the dynamics of demand environments. As a result, the suggested model shows better performance in explaining the diffusion of new-generation product with limited number of data observations. (c) 2003 Elsevier Inc. All rights reserved.
Publisher
ELSEVIER SCIENCE INC
Issue Date
2005-09
Language
English
Article Type
Article
Keywords

SUCCESSIVE GENERATIONS; SEMICONDUCTOR INDUSTRY; MODEL; SUBSTITUTION; DRAMS

Citation

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, v.72, pp.825 - 849

ISSN
0040-1625
DOI
10.1016/j.techfore.2003.09.003
URI
http://hdl.handle.net/10203/15871
Appears in Collection
MG-Journal Papers(저널논문)
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