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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.
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Package details |
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| Author | Hassan Doosti [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-0290-8122>, affiliation: School of Mathematical and Physical Sciences, Macquarie University), Yogendra P. Chaubey [ctb, cph] (original R code of the quantile density estimators and cross-validation criteria, revised and vectorised in R/estimators.R and R/bandwidth.R), Isha Dewan [ctb, cph] (original R code of the quantile density estimators and cross-validation criteria, revised and vectorised in R/estimators.R and R/bandwidth.R), Jun Li [ctb, cph] (original R code of the quantile density estimators and cross-validation criteria, revised and vectorised in R/estimators.R and R/bandwidth.R), Brent R. Hickman [ctb, cph] (original MATLAB implementation of the boundary-corrected kernel density estimator, translated to R in kde_hh()), Timothy P. Hubbard [ctb, cph] (original MATLAB implementation of the boundary-corrected kernel density estimator, translated to R in kde_hh()), Brani Vidakovic [ctb, cph] (original MATLAB routines Phijk, dwtr, Idwtr and loc_lin, translated to R in scaling_function(), phi_jk(), dwt_periodic(), idwt_periodic() and loclin_smooth()), Vadim Marmer [ctb, cph] (original MATLAB implementation of the quantile-based estimator, translated to R in density_marmer_shneyerov(); reference implementation used to validate fpa_density_ci()), Jun Ma [ctb] (reference implementation of inference for the GPV estimator, used to validate fpa_density_ci()), Artyom Shneyerov [ctb, cph] (original MATLAB implementation of the quantile-based estimator, translated to R in density_marmer_shneyerov()), Federico Zincenko [ctb, cph] (original implementation of the kernel estimator of private values with boundary extrapolation, adapted in fpa_values(method = 'zincenko')) |
| Maintainer | Hassan Doosti <Hassan.doosti@mq.edu.au> |
| License | GPL (>= 3) |
| Version | 0.3.0 |
| URL | https://github.com/DoostiH/qdfauction |
| Package repository | View on CRAN |
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