Package: qdfauction 0.2.0

Hassan Doosti

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.

Authors:Hassan Doosti [aut, cre, cph], Yogendra P. Chaubey [ctb, cph], Isha Dewan [ctb, cph], Jun Li [ctb, cph], Brent R. Hickman [ctb, cph], Timothy P. Hubbard [ctb, cph], Brani Vidakovic [ctb, cph], Vadim Marmer [ctb, cph], Jun Ma [ctb], Artyom Shneyerov [ctb, cph], Federico Zincenko [ctb, cph]

qdfauction_0.2.0.tar.gz
qdfauction_0.2.0.zip(r-4.7-any)qdfauction_0.2.0.zip(r-4.6-any)qdfauction_0.2.0.zip(r-4.5-any)
qdfauction_0.2.0.tgz(r-4.6-any)qdfauction_0.2.0.tgz(r-4.5-any)
qdfauction_0.2.0.tar.gz(r-4.7-any)qdfauction_0.2.0.tar.gz(r-4.6-any)
qdfauction_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
qdfauction/json (API)

# Install 'qdfauction' in R:

install.packages('qdfauction', repos = c('https://doostih.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/doostih/qdfauction/issues

Datasets:

timber - USFS timber sealed-bid auctions

On CRAN:

4.00 score 36 downloads 62 exports 0 dependencies

Last updated from:75a1e57217. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	131
source / vignettes	OK	154
linux-release	OK	140
macos-release	OK	133
macos-oldrel	OK	111
windows-devel	OK	108
windows-release	OK	105
windows-oldrel	OK	110
wasm-release	OK	74

Exports:bcv_criterion check_kernel copula_delta copula_delta_prime copula_fit copula_psi copula_psi_np copula_spec_test copula_tau_to_theta copula_theta_to_tau density_marmer_shneyerov dwt_periodic fpa_bid_function fpa_corner_test fpa_density fpa_density_boot fpa_density_ci fpa_density_cv fpa_evaluate_density fpa_evaluate_values fpa_montecarlo fpa_optimal_reserve fpa_optimal_reserve_model fpa_optimal_reserve_qd fpa_revenue fpa_revenue_boot fpa_revenue_model fpa_revenue_qd fpa_simulate fpa_ugrid fpa_values fpa_virtual_value fpa_virtual_value_model idwt_periodic kde_hh loclin_smooth make_kernel phi_jk phi_per psi_jk psi_per qdf qdf_bernstein qdf_bernstein_loo qdf_indirect_poisson qdf_jones qdf_kernel qdf_kernel_corrected qdf_poisson qdf_soni qdf_wavelet qdf_wavelet_nl quantile_indirect_poisson rcopula_archimedean rlcv_criterion scaling_function select_bandwidth select_fpa_density select_wavelet threshold_coefficients wavelet_filter wbcv_criterion

Dependencies:

Structural analysis of first-price auctions with qdfauction: the USFS timber data

Rendered fromtimber.Rmdusingknitr::rmarkdown

Last update: 2026-10-22
Started: 2026-10-08

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Cross-validation criteria for quantile density estimators	bcv_criterion rlcv_criterion wbcv_criterion
Pseudo-maximum-likelihood estimation of an Archimedean copula from bids	copula_fit
Copula multiplier and diagonal functions of exchangeable Archimedean copulas	copula_delta copula_delta_prime copula_psi
Nonparametric estimator of the copula multiplier	copula_psi_np
Specification test for a parametric copula of the private values	copula_spec_test
Convert between Kendall's tau and the copula parameter	copula_tau_to_theta copula_theta_to_tau
Quantile-based density estimator of Marmer and Shneyerov (2012)	density_marmer_shneyerov
Periodic discrete wavelet transform	dwt_periodic idwt_periodic
Equilibrium bid function of a symmetric first-price auction	fpa_bid_function
Test for a corner solution of the optimal reserve under affiliation	fpa_corner_test print.fpa_corner_test
Nonparametric estimation of the private-value density in first-price auctions	fpa_density
Bootstrap confidence intervals and bands for any private-value density estimator	fpa_density_boot plot.fpa_density_boot
Confidence intervals and uniform confidence bands for the private-value density	fpa_density_ci
Compare density estimators by held-out log-likelihood	fpa_density_cv
Evaluate recovered values or an estimated density against the truth	fpa_evaluate_density fpa_evaluate_values
Monte Carlo comparison of estimators	fpa_montecarlo
Optimal reserve price from recovered private values	fpa_optimal_reserve
Plug-in estimator of the seller's expected revenue	fpa_revenue
Bootstrap inference for the expected-revenue estimator	fpa_revenue_boot
Expected revenue and optimal reserve price of a known model	fpa_optimal_reserve_model fpa_revenue_model
Expected revenue and optimal reserve by the quantile-density functional	fpa_optimal_reserve_qd fpa_revenue_qd
Simulate first-price sealed-bid auctions	fpa_simulate
Recover private values from first-price auction bids	fpa_values
Affiliated virtual value	fpa_virtual_value fpa_virtual_value_model
Boundary-corrected kernel density estimator of Hickman and Hubbard (2015)	kde_hh
Local-linear smoother with Gaussian kernel	loclin_smooth
Construct and check a kernel for the kernel-type estimators	check_kernel make_kernel
Evaluate scaling functions phi_{j,k}(z) = 2^{j/2}phi(2^j z - k)	phi_jk
Plot an estimated private-value density	plot.fpa_density
Plot a GPV density estimate with confidence bands	plot.fpa_density_ci
Plot a revenue curve with bootstrap bands	plot.fpa_revenue_boot
Plot recovered private values against bids	plot.fpa_values
Plot a quantile density estimate	plot.qdf
Predict from a fitted quantile density estimate	predict.qdf
Mother wavelet and periodised wavelet basis on the unit interval	phi_per psi_jk psi_per
Nonparametric estimation of a quantile density function	qdf
Indirect estimators of the quantile density	qdf_indirect_poisson qdf_jones qdf_soni
Direct kernel estimators of the quantile density	qdf_kernel qdf_kernel_corrected
Series estimators of the quantile density	qdf_bernstein qdf_bernstein_loo qdf_poisson
Wavelet estimators of the quantile density	qdf_wavelet
Nonlinear wavelet estimators of the quantile density with theoretical thresholds	qdf_wavelet_nl
Simulate exchangeable Archimedean copula scores	rcopula_archimedean
Select the smoothing parameter of a quantile density estimator	select_bandwidth
Cross-validated choice of the wavelet resolution level and bandwidth	select_fpa_density
Cross-validated choice of (j0, h) for the wavelet estimator	select_wavelet
Threshold wavelet coefficients	threshold_coefficients
USFS timber sealed-bid auctions (homogenised bids)	timber
Orthonormal wavelet filters	wavelet_filter