In the presence of intertemporal substitution, static demand estimation yields biased estimates and fails to recover long run price responses. The goal of this paper is to present a computationally simple way to estimate dynamic demand using aggregate data. Previous work on demand dynamics has been computationally intensive and data demanding.
We estimate our model using store level data on soft drinks and find a disparity between static and long run estimates of price responses.
Alternatives solutions offered in the literature perform poorly. The simplicity of the proposed model of storage allows us to start exploring the supply side.