2024/12/01 by Safonov, Kirill · 1 citation
Business, Management and Accounting · Decision Sciences · #Consumer Market Behavior and Pricing #Consumer Retail Behavior Studies #FOS: Economics and business #Forecasting Techniques and Applications #General Economics (econ.GN)
paper · pdf · doi:10.48550/arxiv.2412.00920
openalex publication_date 2024/12/01 · openalex created_date 2024/12/05 · openalex updated_date 2026/07/28
This paper contributes to the literature on parametric demand estimation by using deep learning to model consumer preferences. Traditional econometric methods often struggle with limited within-product price variation, a challenge addressed by the proposed neural network approach. The proposed method estimates the functional form of the demand and demonstrates higher performance in both simulations and empirical applications. Notably, under low price variation, the machine learning model outperforms econometric approaches, reducing the mean squared error of initial price parameter estimates by nearly threefold. In empirical setting, the ML model consistently predicts a negative relationship between demand and price in 100% of cases, whereas the econometric approach fails to do so in 20% of cases. The suggested model incorporates a wide range of product characteristics, as well as prices of other products and competitors.