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Erscheinungsjahr: 
2015
Quellenangabe: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 6 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2015 [Pages:] 429-461
Verlag: 
The Econometric Society, New Haven, CT
Zusammenfassung: 
I propose a Bayesian method to analyze bid data from first-price auctions under private value paradigms. I use a series representation to specify the valuation density so that bidding monotonicity is always satisfied, and I impose density affiliation by the nonparametric technique of Beresteanu (2007). This flexible method is, therefore, fully compatible with the underlying economic theory. To handle such a rich specification, I use a simulated likelihood, yet obtain a correct posterior by regarding the draws used for simulation as a latent variable to be augmented in the Bayesian framework; see Flury and Shephard, 2011. I provide a step-by-step guide of the method, report its performance from various perspectives, and compare the method with the existing one for a range of data generating processes and sample sizes. Finally, I analyze a bid sample for drilling rights in the outer continental shelf that has been widely studied and propose a reserve price that is decision theoretically optimal under parameter uncertainty.
Schlagwörter: 
First price sealed bid auctions
affiliated private values
revenue maximizing reserve price
Bayesian analysis
method of series
simulated likelihood
shape restriction
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