Empirical Relevance of Ambiguity in First Price Auction Models

Gaurab Aryal, Dong-Hyuk Kim
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引用次数: 4

Abstract

We study the identification and estimation of first-price auction models where bidders have ambiguity about the valuation distribution and their preferences are represented by maxmin expected utility. When entry is exogenous, the distribution and ambiguity structure are nonparametrically identified, separately from risk aversion (CRRA). We propose a flexible Bayesian method based on Bernstein polynomials. Monte Carlo experiments show that our method estimates parameters precisely, and chooses reserve prices with (nearly) optimal revenues, whether there is ambiguity or not. Furthermore, if the model is misspecified -- incorrectly assuming no ambiguity among bidders -- it may induce estimation bias with a substantial revenue loss.
第一价格拍卖模型中模糊性的实证相关性
本文研究了投标人对估价分布存在模糊性且其偏好由最大期望效用表示的首价拍卖模型的识别和估计问题。当进入是外生的,分布和模糊结构被非参数识别,与风险厌恶(CRRA)分开。提出了一种基于Bernstein多项式的灵活贝叶斯方法。蒙特卡罗实验表明,无论是否存在歧义,我们的方法都能精确地估计参数,并选择具有(接近)最优收益的保留价格。此外,如果模型被错误地指定——错误地假设竞标者之间没有歧义——它可能会导致估计偏差,导致大量的收入损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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