Hybrid demand forecasting: integrating behavioral economics with econometric and machine learning models

Abstract

Accurate demand forecasting is critical for firm-level operational and strategic decisions, yet conventional econometric and machine learning models largely abstract from systematic behavioral distortions in consumer decision-making. Drawing on behavioral demand theory, this study develops and empirically evaluates a hybrid forecasting framework that integrates reference dependence, habit persistence, and attention-based mechanisms into econometric, machine learning, and ensemble demand forecasting models. Using firm-level demand data and a rolling-origin validation design, we compare traditional baseline models with behaviorally augmented specifications across multiple forecast horizons and error metrics. The results show that models incorporating behavioral variables consistently and significantly outperform standard econometric and machine learning benchmarks out of sample. Reference price losses exert a substantially stronger predictive influence than gains, consistent with loss aversion, while habit persistence dominates short-horizon forecasts and attention and sentiment measures contribute most at medium horizons. Further gains are achieved through forecast ensembles that combine behaviorally augmented econometric and machine learning models, indicating complementary strengths in structural discipline and non-linear approximation. These findings demonstrate that behavioral demand mechanisms are not only explanatory but also predictively relevant at the firm level. The study contributes to behavioral demand theory by extending it into an explicitly predictive context, advances forecasting methodology by showing the value of theory-guided feature augmentation, and offers a scalable framework for firms seeking more accurate and behaviorally informed demand forecasts.

Keywords: Behavioral Demand, Econometric Models, Forecast Ensembles, Habit Persistence, Loss Aversion.