Disentangling Risk-Aversion and Loss Aversion in First-Price Auctions : An Empirical Approach

We study a model that combines bidder-specific risk-averse preferences with anticipated loss aversion to explain bidding in induced value first-price auctions. We first show that, like risk-aversion, loss aversion causes ‘overbidding,’ relative to the bidding without risk and loss aversion. We then establish the identification of the model primitives, including the loss aversion coefficient and distribution of risk-aversion coefficients. In addition, we develop a Bayesian method to estimate the model primitives, integrating out bidder-specific risk-aversion coefficients via data augmentation. Our method predicts the data well, and the counterfactual analysis shows that loss aversion explains 56 ∼ 90% of overbidding in the data