Auctions and procurements are used for a wide range of private and public transactions. They offer conceptually simple mechanisms for price determination in multilateral trading. They are typically under well-defined rules and rich data sets are available (though not necessarily at negligible direct and indirect costs, notwithstanding important issues of confidentiality). Despite an increasing number of innovative empirical studies of auctions, there remains a substantial gap between theory and applications. The reasons for this include: the questionable empirical validity of key assumptions; the fundamental issue of whether the participants in auctions actually are capable of exercising the exceptionally high degree of rationality assumed by theory; the inherent complexity of the mapping between unobservable individual valuations and the related difficulties associated with `structure` estimation of empirical auction models. This research project aims at developing an integrated approach for the empirical analysis of auction data that addresses the gap between theory and applications. The key components of this approach are: The modeling of observed bidding practices in the form of `rules of thumbs` as operational and intuitively appealing alternatives to the complex Nash equilibrium strategies prescribed by theory; The use of Monte Carlo simulation techniques to find those rules of thumb that perform `well` in the context of a specific empirical auction problem; The development of a generic estimation principle capable of accommodating a broad clan of structural models obtained by application of (1) and (2) allowing, in particular, for parameteric, non parametric and semi-parametric stochastic formulations as deemed appropriate in the context of specific applications; and Empirical applications using data sets from the timber industry in the Pacific Northwest region and from the French aerospatial industry.
