Integrable Elasticity via Neural Demand Potentials

TL;DR

The paper introduces ICDN, a neural model that accurately learns demand functions to derive elasticities, improving generalization and stability in retail demand estimation.

Contribution

It presents a novel demand-first neural network that enables exact elasticity derivation and better generalization in multiproduct retail demand modeling.

Findings

ICDN outperforms benchmark models on the Dominick's beer dataset.

ICDN provides more stable and economically plausible elasticity estimates.

The model improves out-of-sample demand prediction accuracy.

Abstract

We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned demand surface. On the Dominick's beer dataset, ICDN improves out-of-sample generalization over a directed log-log benchmark and yields more stable, economically plausible elasticity estimates, especially for weakly identified cross-price effects.