Account-Level Modeling for Trade Promotion: An Application of a Constrained Parameter Hierarchical Model
零售客户层面的贸易促销建模:约束参数层级模型的应用AI 解读 ↓
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We consider the problem of utilizing data at the retail/market level on sales and marketing mix variables to help manufacturers optimize the allocation of trade promotional budgets across areas. Major consumer packaged goods manufacturers budget at least one-half of their total marketing expenses to trade promotions. Trade promotional deals are designed to encourage retailers to promote products by temporarily reducing the price, putting them in in-store displays, or advertising in local media. A profit-based trade promotional allocation system will require estimates of the responsiveness of sales at each retailer to a given promotion. A major barrier to the use of retailer data is the proliferation of incorrectly signed coefficients in standard least squares analyses. Even more sophisticated adaptive shrinkage methods will not remove the problem of improper signs. We propose a hierarchical model to modeling retailer response that uses a first-stage prior with inequality constraints on the regression coefficients. We demonstrate the usefulness of our modeling approach with data on more than 75 retailers. We find substantial profit opportunities from our response-based promotional allocation scheme over and above what might be achieved by a standard volume-oriented allocation scheme.
Two frequent approaches for identifying structural VARs are external instruments, which carry economic content but are often weak, and non-Gaussianity of the shocks, which provides statistical identification but carries no economic meaning. We combine the two strategies in a single generalized method of moments framework. This hybrid approach point-identifies the target shocks while also identifying the non-target shocks up to sign and ordering. Under suitable rank…
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We study the role of design smoothness in the minimax estimation of conditional average treatment effects at a fixed covariate point. For design densities bounded above and away from zero, without further regularity, we derive upper and lower risk bounds with an arbitrarily small gap in the convergence-rate exponent. Under uniform design, we obtain matching upper and lower bounds at interior points in the regime of low nuisance smoothness. These results show that rough…
多层主效应矩阵因子模型
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高维稀疏组卷积平滑分位数回归
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PICPIs:预测区间条件预测区间
A classical question in statistics is which observable quantities to condition on when drawing inferences about unobservable targets. For conformal prediction in nonparametric uncertainty quantification, standard marginal validity offers limited resolution at the prediction values on which decisions are based, and fully conditional guarantees with respect to the covariates are provably unattainable. We address this gap by introducing a prediction-based conditioning framework…
Bayesian additive regression trees (BART) is a popular Bayesian ensemble model used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpreted as weak learners. In this work, we propose a generalization of the BART model that has two main features: first, it automatically selects the number of decision trees using the given data; second, the model allows clusters of…