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Nonstationary Brand Variables in Category Management: A Cointegration Perspective

机译：类别管理中的非平稳品牌变量：协整观点

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摘要

Category-management models serve to assist in the development of plans for pricing and promotions of individual brands. Techniques to solve the models can have problems of accuracy and interpretability because they are susceptible to spurious regression problems due to nonstationary time-series data. Improperly stated nonstationary systems can reduce the accuracy of the forecasts and undermine the interpretation of the results. This is problematic because recent studies indicate that sales are often a nonstationary time-series. Newly developed correction techniques can account for nonstationarity by incorporating error-correction terms into the model when using a Bayesian Vector Error-Correction Model. The benefit of using such a technique is that shocks to control variates can be separated into permanent and temporary effects and allow cointegration of series for analysis purposes. Analysis of a brand data set indicates that this is important even at the brand level. Thus, additional information is generated that allows a decision maker to examine controllable variables in terms of whether they influence sales over a short or long duration. Only products that are nonstationary in sales volume can be manipulated for long-term profit gain, and promotions must be cointegrated with brand sales volume. The brand data set is used to explore the capabilities and interpretation of cointegration.
机译：类别管理模型可协助制定单个品牌的定价和促销计划。解决模型的技术可能会存在准确性和可解释性的问题，因为由于不稳定的时间序列数据，它们容易出现虚假的回归问题。陈述不正确的非平稳系统会降低预测的准确性，并破坏结果的解释。这是有问题的，因为最近的研究表明销售通常是一个不稳定的时间序列。当使用贝叶斯矢量误差校正模型时，通过将误差校正项合并到模型中，新开发的校正技术可以解决非平稳性问题。使用这种技术的好处是可以将控制变量的冲击分为永久性影响和暂时性影响，并可以出于分析目的对序列进行协整。对品牌数据集的分析表明，即使在品牌级别，这一点也很重要。因此，生成了其他信息，使决策者可以根据可控制变量在短期还是长期内影响销售来检查这些变量。只能操纵销量不稳定的产品以获取长期利润，促销必须与品牌销量保持一致。品牌数据集用于探索协整的功能和解释。

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