Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/150390
Authors: 
Kim, Dong-Hyuk
Year of Publication: 
2015
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 6 [Year:] 2015 [Issue:] 2 [Pages:] 429-461
Abstract: 
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.
Subjects: 
First price sealed bid auctions
affiliated private values
revenue maximizing reserve price
Bayesian analysis
method of series
simulated likelihood
shape restriction
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Article

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