Flexible Bayesian analysis of first price auctions using a simulated likelihood

• 成果类型: Article
• 署名作者: Kim, Dong-Hyuk
• 署名单位: Vanderbilt University
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE257
• 发表日期: 2015
• 页码: 429-461
• 关键词: First price sealed bid auctions; affiliated private values; revenue maximizing reserve price; Bayesian analysis; method of series; simulated likelihood; shape restriction

摘要

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.