Quantile regression methods for first-price auctions

• Tác giả: Nathalie Gimenes, Emmanuel Guerre
• Ngôn ngữ: eng
• Ký hiệu phân loại: 018.3 +Catalogs arranged by author, main entry, date, or register number
• Thông tin xuất bản: 2019
• Bộ sưu tập: Metadata
• ID: 163348

Tóm tắt

Comment: This revision focuses on the quanile regression specification and includes new developments on testing (also considered in the application) and a comparison with random coefficient models. The simulation experiment proposes a comparison with GPV and HHS. The new sieve appendix has been revised and improvedThe paper proposes a quantile-regression inference framework 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 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. This also allows to test for various specification or exogeneity null hypothesis using the observed bids in a simple way. 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. Linear interactive sieve extensions are proposed and studied in the Appendices.