### Statistics Division, Linköping University

Shared publicly -Next research seminar is on Tuesday, May 22, 3.15 pm

Topic: Bayesian inference in Structural Second-Price Auctions With Both Private-value and Common-value Bidders

Presenter: Bertil Wegmann, Statistics, Linköping University.

Abstract: Auctions with asymmetric bidders have been actively studied in recent years. Tan and Xing (2011) show the existence of monotone pure-strategy equilibrium in auctions with both private-value and common-value bidders. The equilibrium bid function is given as the solution to an ordinary differential equation (ODE). We approximate the ODE and obtain a very accurate, approximate inverse bid as an explicit function of a given bid. We propose a model where the valuations of both common-value and private-value bidders are functions of covariates. The probability of being a common-value bidder is modeled by a logistic regression model with Bayesian variable selection. The model is estimated on a dataset of eBay coin auctions. We analyze the model using Bayesian methods implemented via a Metropolis-within-Gibbs algorithm.

Location: Alan Turing.

See http://www.ida.liu.se/divisions/stat/seminarier/ for info about past and future seminars.

Topic: Bayesian inference in Structural Second-Price Auctions With Both Private-value and Common-value Bidders

Presenter: Bertil Wegmann, Statistics, Linköping University.

Abstract: Auctions with asymmetric bidders have been actively studied in recent years. Tan and Xing (2011) show the existence of monotone pure-strategy equilibrium in auctions with both private-value and common-value bidders. The equilibrium bid function is given as the solution to an ordinary differential equation (ODE). We approximate the ODE and obtain a very accurate, approximate inverse bid as an explicit function of a given bid. We propose a model where the valuations of both common-value and private-value bidders are functions of covariates. The probability of being a common-value bidder is modeled by a logistic regression model with Bayesian variable selection. The model is estimated on a dataset of eBay coin auctions. We analyze the model using Bayesian methods implemented via a Metropolis-within-Gibbs algorithm.

Location: Alan Turing.

See http://www.ida.liu.se/divisions/stat/seminarier/ for info about past and future seminars.

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