Please use this identifier to cite or link to this item: https://doi.org/10.1287/mksc.1040.0056
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
Source: 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. https://doi.org/10.1287/mksc.1040.0056
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
URI: http://scholarbank.nus.edu.sg/handle/10635/43870
ISSN: 07322399
DOI: 10.1287/mksc.1040.0056
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

48
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

41
checked on Nov 21, 2017

Page view(s)

59
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.