Research
Publication
Semiparametric Quantile Models for Ascending Auctions with Asymmetric Bidders (with Nathalie Gimenes (PUC-Rio) and Emmanuel Guerre (QMUL)) (Journal of Business & Economic Statistics (2022), 40(3), pp.1020-1033 )
The paper proposes a parsimonious and flexible semiparametric quantile regression specification for asymmetric bidders within the independent private value framework. Asymmetry is parameterized using powers of a parent private value distribution, which is generated by a quantile regression specification. As noted in Cantillon (2008), this covers and extends models used for efficient collusion, joint bidding and mergers among homogeneous bidders. The specification can be estimated for ascending auctions using the winning bids and the winner’s identity. The estimation is in two stage. The asymmetry parameters are estimated from the winner’s identity using a simple maximum likelihood procedure. The parent quantile regression specification can be estimated using simple modifications of Gimenes (2017). Specification testing procedures are also considered. A timber application reveals that weaker bidders have 30\% less chances to win the auction than stronger ones. It is also found that increasing participation in an asymmetric ascending auction may not be as beneficial as using an optimal reserve price as would have been expected from a result of Bulow and Klemperer (1996) valid under symmetry.
Working Papers
Quantile Regression with Generated Dependent Variable and Covariates (Updated draft soon)
This paper studies linear quantile regression models when regressors and/or dependent variable are not directly observed but estimated in an initial first step and used in the second step quantile regression for estimating the quantile parameters. This general class of generated quantile regression (GQR) covers various statistical applications, for instance, estimation of endogenous quantile regression models and triangular structural equation models, and some new relevant applications are discussed. We study the asymptotic distribution of the two-step estimator, which is challenging because of the presence of generated covariates and/or dependent variable in the non-smooth quantile regression estimator. We employ techniques from empirical process theory to find uniform Bahadur expansion for the two step estimator, which is used to establish the asymptotic results. We illustrate the performance of the GQR estimator in a simulation exercise and an empirical application based on auctions.
The Key Class in Networks (with Nizar Allouch, University of Kent ) (Revise & Resubmit)
This paper proposes new centrality measures to characterise the 'key class', when agents in a network are sorted into role-equivalent classes, such that its removal results in an optimal change in the network activity. The notion of role-equivalence is defined through the graph-theoretical concept of equitable partition of networks, which finds wide empirical and theoretical applicability. Players in the network engage in a non-cooperative game with local payoff complementarities. We establish a link between the generic network and its partitioned or quotient graph, and use it to relate the Nash equilibrium activity of classes with their position within the partitioned network. The result informs two class-based centrality measures that geometrically characterise the key class for an optimal reduction (or increase) in the aggregate and the per-capita network activity, respectively.