This paper deals with inferring key parameters on marketing response at a true high frequency while data are partly or fully available only at a lower frequency aggregate levels. The familiar Koyck model turns out to be very useful for this purpose. Assuming this model for the high-frequency data makes it possible to infer the high-frequency parameters from modified Koyck type models when lower frequency data are available. This means that inference using the Koyck model is robust to temporal aggregation.

中文翻译：

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营销响应和时间汇总

本文讨论了在真正高频率下推销营销响应的关键参数，而只有在较低频率总水平下才能部分或全部获得数据的情况。事实证明，熟悉的Koyck模型对于此目的非常有用。假设此模型用于高频数据，则可以在低频数据可用时从修改的Koyck型模型推断出高频参数。这意味着使用Koyck模型的推断对于时间聚合是鲁棒的。