A Direct Aggregation Approach to Inferring Microparameters of the Koyck Advertising-Sales Relationship from Macro Data
研究了在仅有宏观数据时，如何通过直接加总模型和约束搜索估计法，更准确地推断科伊克广告-销售关系中的微观参数，避免了已有模型中的参数不可行问题。
The authors examine the advertising-sales relationship in the framework of the Koyck model. They note that if only macro (e.g., annual) data are available, it is necessary to approximate micro (e.g., monthly) data in order to minimize the “data interval bias” in estimating the microparameters. They examine two previously proposed models for minimizing the data interval bias and propose a direct aggregation model that avoids some of the problems common to those two models. The proposed constrained search estimation method eliminates the problem of infeasible parameter estimates present in the OLS estimation of the two previous models. A comparison of the constrained search estimation of the three models in a simulation setting indicates that the proposed model recovers the microparameters more accurately than the other two models.