Essays on Nonparametric Identification and Estimation of All-Pay Auctions and Contests

My dissertation contributes to the structural nonparametric econometrics of auctions and contests with incomplete information. It consists of three chapters.The first chapter investigates the identification and estimation of an all-pay auction where the object is allocated to the player with the highest bid, and every bidder pays his bid regardless of whether he wins or not. As a baseline model, I consider the setting, where one object is allocated among several risk-neutral participants with independent private values (IPV); however, I also show how the model can be extended to the multiunit case. Moreover, the model is not confined to the IPV paradigm, and I further consider the case where the bidders’ private values are affiliated (APV). In both IPV and APV settings, I prove the identification and derive the consistent estimators of the distribution of the bidders’ valuations using a structural approach similar to that of Guerre et al. (2000). Finally, I consider the model with risk-averse bidders. I prove that in general the model in this set-up is not identified even in the semi-parametric case where the utility function of the bidders is restricted to belong to the class of functions with constant absolute risk aversion (CARA).The second chapter proves the identification and derives the asymptotically normal estimator of a nonparametric contest of incomplete information with uncertainty. By uncertainty, I mean that the contest success function is not only determined by the bids of the players, but also by the variable, which I call uncertainty, with a nonparametric distribution, unknown to the researcher, but known to the bidders. This work is the first to consider the incomplete information contest with a nonparametric contest success function. The limiting case of the model when there is no uncertainty is an all-pay auction considered in the first chapter. The model with two asymmetric players is examined. First, I recover the distribution of uncertainty using the information on win outcomes and bids. Next, I adopt the structural approach of Guerre et al. (2000) to obtain the distribution of the bidders’ valuations (or types). As an empirical application, I study the U.S. House of Representatives elections. The model provides a method to disentangle two sources of incumbency advantage: a better reputation, and better campaign financing. The former is characterized by the distribution of uncertainty and the latter by the difference in the distributions of candidates’ types. Besides, two counterfactual analyses are performed: I show that the limiting expenditure dominates public campaign financing in terms of lowering total campaign spending as well as the incumbent’s winning probability.The third chapter is a semiparametric version of the second chapter. In the case when the data is sparse, some restrictions on the nonparametric structure need to be put. In this work, I prove the identification and derive the consistent estimator of a contest of incomplete information, in which an object is allocated according to the serial contest success function. As in previous chapters, I recover the distribution of the bidders’ valuations from the data on observed bids using a structural approach similar to that of Guerre et al. (2000) and He and Huang (2018). As a baseline model, I consider the symmetric contest. Further, the model is extended to account for the bidders’ asymmetry

Bid Coordination in Sponsored Search Auctions: Detection Methodology and Empirical Analysis

Bid delegation to specialized intermediaries is common in the auction systems used to sell internet advertising. When the same intermediary concentrates the demand for ad space from competing advertisers, its incentive to coordinate client bids might alter the functioning of the auctions. This study develops a methodology to detect bid coordination, and presents a strategy to estimate a bound on the search engine revenue losses imposed by coordination relative to a counterfactual benchmark of competitive bidding. Using proprietary data from auctions held on a major search engine, coordination is detected in 55 percent of the cases of delegated bidding that we observed, and the associated upper bound on the search engine’s revenue loss ranges between 5.3 and 10.4 percent

Nonparametric identification and estimation of all-pay auction and contest models

In this paper, I study the nonparametric identification and estimation of multi-unit all-pay auctions of incomplete information. First, I consider the setting where multiple goods are allocated among several risk-neutral participants with independent private values (IPV). I prove the nonparametric identification of the model and derive two different consistent estimators of the distribution of bidder valuations. The first estimator is based on the classical structural approach similar to that of Guerre et al. (Econometrica 68(3):525-574, 2000). The second estimator, instead, allows estimation of the quantile function of the bidders' valuations directly using the quantile density of the bids. Monte Carlo simulations show good small sample property under various assumptions of the number of players and goods. Next, I consider a variety of model extensions: the case of affiliated private values (APV), asymmetric players, the addition of random noise, as well as the case of risk-averse bidders. In contrast to all other scenarios, I prove that the general model with risk-averse bidders is not identified even in the semi-parametric case in which utility function is restricted to belong to the class of functions with constant absolute risk aversion (CARA). On the other hand, I show that the model with risk aversion can be identified if the distribution of valuations is restricted to having fixed support

Bid coordination in sponsored search auctions: detection methodology and empirical analysis

Bid delegation to specialized intermediaries is common in the auction systems used to sell internet advertising. When the same intermediary concentrates the demand for ad space from competing advertisers, its incentive to coordinate client bids might alter the functioning of the auctions. This study develops a methodology to detect bid coordination, and presents a strategy to estimate a bound on the search engine revenue losses imposed by coordination relative to a counterfactual benchmark of competitive bidding. Using proprietary data from auctions held on a major search engine, coordination is detected in 55 percent of the cases of delegated bidding that we observed, and the associated upper bound on the search engine’s revenue loss ranges between 5.3 and 10.4 percent