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Kalman Filter Estimation of New Product Diffusion Models
Jinhong Xie, X. Michael Song, Marvin Sirbu and Qiong Wang
Journal of Marketing Research
Vol. 34, No. 3 (Aug., 1997), pp. 378-393
Published by: American Marketing Association
Stable URL: http://www.jstor.org/stable/3151900
Page Count: 16
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The authors introduce a new estimation procedure, Augmented Kalman Filter with Continuous State and Discrete Observations (AKF(C-D)), for estimating diffusion models. This method is directly applicable to differential diffusion models without imposing constraints on the model structure or the nature of the unknown parameters. It provides a systemtic way to incorporate prior knowledge about the likely values of unknown parameters and updates the estimates when new data become available. The authors compare AKF(C-D) empirically with five other estimation procedures, demonstrating AKF(C-D)'s superior prediction performance. As an extension to the basic AKF(C-D) approach, they also develop a parallel-filters procedure for estimating diffusion models when there is uncertainty about diffusion model structure or prior distributions of the unknown parameters.
Journal of Marketing Research © 1997 American Marketing Association