Artyom Shneyerov

The rate of convergence to perfect competition of matching and bargaining mechanisms

We study the steady state of a market with incoming cohorts of buyers and sellers who are matched pairwise and bargain under private information. A friction parameter is [tau], the length of the time period until the next meeting. We provide a necessary and sufficient condition for the convergence of mechanism outcomes to perfect competition at the linear rate in [tau], which is shown to be the fastest possible among all bargaining mechanisms. The condition requires that buyers and sellers always retain some bargaining power. The bargaining mechanisms that satisfy this condition are called nonvanishing bargaining power (NBP) mechanisms. Simple random proposer take-it-or-leave-it protocols are NBP, while k-double auctions (k-DA) are not. We find that k-DAs have equilibria that converge to perfect competition at a linear rate, converge at a slower rate or even do not converge at all.

Identification in first-price and Dutch auctions when the number of potential bidders is unobservable

Within the IPV paradigm, we show nonparametric identification of model primitives for first-price and Dutch auctions with a binding reserve price and auction-specific, unobservable sets of potential bidders.

Identifying Collusion in English Auctions

We develop a fully nonparametric identification framework and a test of collusion in ascending bid auctions. Assuming efficient collusion, we show that the underlying distributions of values can be identified despite collusive behaviour when there is at least one bidder outside the cartel. We propose a nonparametric estimation procedure for the distributions of values and a bootstrap test of the null hypothesis of competitive behaviour against the alternative of collusion. Our framework allows for asymmetric bidders, and the test can be performed on individual bidders. The test is applied to the Guaranteed Investment Certificate auctions conducted by US municipalities over the Internet. Despite the fact that there have been allegations of collusion in this market, our test does not detect deviations from competition. A plausible explanation of this finding is that the Internet auction design involves very limited information disclosure.

Inference for First-Price Auctions with Guerre, Perrigne, and Vuong's Estimator

In this paper, we focus on inference on the probability density function (PDF) of the valuations in the first-price sealed-bid auction models within the independent private value paradigm in the presence of auction-specific heterogeneity. We show the asymptotic normality of the two-step nonparametric estimator of Guerre et al. (2000, GPV), and propose an easily implementable and consistent estimator of the asymptotic variance of the two-step estimator. In addition, we prove the validity of the percentile bootstrap inference with the GPV estimator.

Supplement to \Quantile-Based Nonparametric Inference for First-Price Auctions"

This paper contains supplemental materials for Marmer and Shneyerov (2010). We discuss here how the approach developed in the aforementioned paper can be applied to conducting inference on the optimal reserve price in first-price auctions, report additional simulations results, and provide a detailed proof of the bootstrap result in Marmer and Shneyerov (2010).