Measuring delayed and long‐term effects of pricing decisions on market share

Terui, Nobuhiko

doi:10.1108/02634500410536867

Cited by 1 publication

(2 citation statements)

References 22 publications

(14 reference statements)

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“…The sum of dynamic multipliers M ðjÞ 1 ¼ P 1 k¼0 M ðjÞ k is called the total multiplier. It indicates the final effects from X to Y in the long-run at regime j: Bronnenberg et al [24] and Terui [25] discuss these concepts for dynamic response in marketing. We have a recursive formula to get these multipliers from the structural parameters in the next section.…”

Section: Modelmentioning

confidence: 99%

“…Then we examine the out of sample performance of multi-step forecasts. One advantage of a Bayesian approach is forecasting because it offers a complete operational [25,39] suggests using the Bayesian predictive density for linear market-share time series models. The optimal predictor with the minimum mean squared error is known as the conditional expectation * Y tþh ¼ EfY tþh j Y tÀ1 ; Y tÀ2 ; .…”

Section: Model Estimationmentioning

confidence: 99%

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Forecasting model with asymmetric market response and its application to pricing of consumer package goods

Terui

Imano

2005

Appl Stoch Models Bus & Ind

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SUMMARYThis paper presents a dynamic forecasting model that accommodates asymmetric market responses to marketing mix variable}price promotion}by threshold models. As a threshold variable to generate a mechanism for different market responses, we use the counterpart to the concept of a price threshold applied to a representative consumer in a store. A Bayesian approach is taken for statistical modelling because of advantages that it offers over estimation and forecasting. The proposed model incorporates the lagged effects of a price variable. Thereby, myriad pricing strategies can be implemented in the time horizon. Their effectiveness can be evaluated using the predictive density. We intend to improve the forecasting performance over conventional linear time series models. Furthermore, we discuss efficient dynamic pricing in a store using strategic simulations under some scenarios suggested by an estimated structure of the models. Empirical studies illustrate the superior forecasting performance of our model against conventional linear models in terms of the root mean square error of the forecasts. Useful information for dynamic pricing is derived from its structural parameter estimates. This paper develops a dynamic forecasting model that accommodates asymmetric market responses to marketing mix variable}price promotion}by the threshold models.

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Section: Modelmentioning

confidence: 99%

Section: Model Estimationmentioning

confidence: 99%