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May 01, 2011 · Mathematically, the Bass diffusion model (see ) is described by the following differential equation: (1) d N (t) d t = p [m − N (t)] + q m N (t) [m − N (t)], N (0) = 0, t ≥ 0, where N (t) is the cumulative number of adopters of a new product at time t, parameter m > 0 is the total market potential for the new product, and parameters p > 0 and q ≥ 0 are the coefficients of innovation and imitation, respectively.

Bass model parameter estimation

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A proven model for adjusting stated purchase intention is utilized to improve the predictive accuracy of initial purchases of new technologies (Jamieson, Linda F. and Bass, Frank M., "Adjusting Stated Intention Measures to Predict Trial Purchase of New Products: A Comparison of Models and Methods," Journal of Marketing Research, August 1989).
 

 

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The Bass Model parameter representing the potential market, which is the ultimate number of purchasers of the product, is constant. It is denoted by. . Time intervals are numbered sequentially with the first full time interval (usually year) of sales at t = 1 in the Srinivasan-Mason 2 form of the Bass Model equations.
 
 

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Bass Model Parameter Estimation Using Regression? Posted by Anonymous on 11/5/2018 at 9:46 AM ET 125 Points Hi, I am trying to find a clear explanation for using regression to estimate the p and q parameters for Bass diffusion modeling. The parameters of the Generalized Bass model (Bass, Krishnan, and Jain 1994) could be estimated via a modified version of the nonlinear regression estimation: we recommend estimating p and q via nonlinear regression, and obtaining the estimates for the impact of marketing effort via managerial judgment.

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The parameter estimations of the discrete Bass model are very simple and precise. The difference equation itself can be used for the ordinary least squares procedure. Parameter estimation using the ordinary least squares procedure is equal to that using the nonlinear least squares procedure in the discrete Bass model.

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