Abstract
In this work we propose a modern statistical approach to the analysis and modeling of dynamics in online auctions. Online auction data usually arrive in the form of a set of bids recorded over the duration of an auction. We propose the use of a modern statistical approach called functional data analysis that preserves the entire temporal dimension in place of currently used methods that aggregate over time thereby losing important information. This enables us to investigate not only the price evolution that takes place during an auction, but also the dynamics of the price evolution. We show how functional data analysis can be combined with cluster analysis and regression-type models for data exploration and summarization, and for testing hypotheses about relationships between price formation and other relevant factors.