Distributed lags using elastic-net regularization for market response models: focus on predictive and explanatory capacity

For many decades, considerable research has been conducted on Market Response models. Mostly without any attempts to validate the results in strictly predictive tasks and often ignoring if the methods comply with the underlying assumptions and conditions, like the method’s ability to outline the broadly accepted effects of advertising actions. This work presents an enhanced method for market response models consistent with the underlying assumptions of such. Our method is based on Distributed Lag Models with the novelty of introducing regularization in its estimation, a cross-validation framework, and hold-out testing, next to present an empirical manner of extracting its effects. This approach allows the construction of models in an exploratory and simple manner, unlocking the possibility of extracting the underlying effects and being suitable for large samples and many variables. Last, we conduct a practical example using real-world data, accompanied by an unprecedented set of empirical explainability assessments next to a high level of predictive capability in similar circumstances to how it would be used for decision-making in a corporate setup.