Quantile regression methods for first-price auctions

The paper proposes a sieve quantile regression approach for first-price auctions with symmetric risk-neutral bidders under the independent private value paradigm. It is first shown that a private value quantile regression model generates a quantile regression for the bids. The private value quantile regression can be easily estimated from the bid quantile regression and its derivative with respect to the quantile level. A new local polynomial technique is proposed to estimate the latter over the whole quantile level interval. Plug in estimation of functionals is also considered, as needed for the expected revenue or the case of CRRA risk-averse bidders, which is amenable to our framework. A quantile regression analysis to USFS timber is found more appropriate than the homogenized bid methodology and illustrates the contribution of each explanatory variables to the private value distribution.