Bids during an online auction arrive at unequally-spaced discrete time points. Our goal is to capture the entire continuous price-evolution function by representing it as a functional object. Various nonparametric smoothing methods exist to recover the functional object from the observed discrete bid data. Previous studies use penalized polynomial and monotone smoothing splines; however, these require the determination of a large number of coefcients and often lengthy computational time. We present a family of parametric growth curves that describe the price-evolution during online auctions. Our approach is parsimonious and has an appealing interpretation in the online auction context. We also provide an automated tting algorithm that is computationally fast. Our method is illustrated on
several eBay datasets.