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Title: Evolutionary estimation of macro-level diffusion models using genetic algorithms: An alternative to nonlinear least squares
Authors: Venkatesan, R.
Krishnan, T.V. 
Kumar, V.
Keywords: Bass model
Closed-form solution
Genetic algorithms
Nonlinear least squares
Pre-peak sales forecasting
Starting values
Systematic change and bias
Issue Date: 2004
Citation: Venkatesan, R., Krishnan, T.V., Kumar, V. (2004). Evolutionary estimation of macro-level diffusion models using genetic algorithms: An alternative to nonlinear least squares. Marketing Science 23 (3) : 451-464+466. ScholarBank@NUS Repository.
Abstract: In this paper, we provide theoretical arguments and empirical evidence for how Genetic Algorithms (GA) can be used for efficient estimation of macro-level diffusion models. Using simulations we find that GA and Sequential Search-Based-Nonlinear Least Squares (SSB-NLS) provide comparable parameter estimates when the data including peak sales are being used, for a range of error variances, and true parameter values commonly encountered in the literature. From empirical analyses we find that the forecasting performance of the GA estimates is better than that of SSB-NLS, Augmented Filter, Hierarchical Bayes, and Kalman Filter when only pre-peak sales data is available for estimation. When sales data until the peak time period are available for estimation, SSB-NLS is able to obtain parameter estimates when the starting values provided are the estimates from using GA. The estimates from GA are not biased and do not change in a systematic fashion when post-peak sales data are used, whereas the estimates from SSB-NLS are biased and change in a systematic fashion. Summarizing, we find that GA may be better suited for diffusion model estimation under the three conditions where SSB-NLS has been found to have problems.
Source Title: Marketing Science
ISSN: 07322399
DOI: 10.1287/mksc.1040.0056
Appears in Collections:Staff Publications

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