qdfauction - Nonparametric Quantile Density Estimation and Structural
Analysis of First-Price Auctions
Nonparametric estimators of the quantile density function
(the derivative of the quantile function): kernel,
boundary-corrected kernel, Poisson-weight and
Bernstein-polynomial smoothers, indirect estimators (Jones
(1992) <https://www.ism.ac.jp/editsec/aism/pdf/044_4_0721.pdf>;
Soni, Dewan and Jain (2012) <doi:10.1016/j.csda.2012.04.014>;
Chaubey, Dewan and Li (2024)
<doi:10.1080/03610926.2023.2222922>), and linear,
hard-thresholded and block-thresholded wavelet estimators
(Chesneau, Dewan and Doosti (2016)
<doi:10.1016/j.csda.2015.08.006>; Shirazi and Doosti (2022)
<doi:10.1080/03610918.2019.1656250>), with smoothing parameters
chosen by biased, robust likelihood (Wu (2019)
<doi:10.1080/07350015.2018.1424633>) or least-squares
cross-validation. The estimators are applied to the structural
analysis of first-price sealed-bid auctions: recovery of
private values for independent or affiliated values and
risk-neutral or risk-averse bidders (Doosti, Dewan and Talebian
(2025) <doi:10.1016/j.econlet.2025.112670>; Doosti (2026)
<doi:10.1016/j.econlet.2026.112837>), estimation of the
private-value density with kernel benchmarks (Hickman and
Hubbard (2015) <doi:10.1002/jae.2385>; Marmer and Shneyerov
(2012) <doi:10.1016/j.jeconom.2011.09.020>; Zincenko (2024)
<doi:10.1016/j.jeconom.2024.105734>) and confidence bands (Ma,
Marmer and Shneyerov (2019)
<doi:10.1016/j.jeconom.2019.02.006>), and estimation of the
seller's expected revenue and optimal reserve price, with tools
for simulating equilibrium bids and comparing estimators.
