Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts
We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and post-launch distributional forecasts. Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product.
Marketplace Expansion Through Marquee Seller Adoption: Externalities and Reputation Implications
In the race to establish themselves, many early-stage online marketplaces choose to accelerate their growth by adding marquee (established brand name) sellers. We study the implications of marquee seller entry on smaller, unbranded sellers in a marketplace when both unbranded sellers and marquee sellers can vary vertically across reputation (referred to as sellers’ quality). While recent literature has shown that higher-quality unbranded sellers fare better than their lower-quality peers, we posit that this may not hold for entrants of any quality.