Package {qdfauction}

Type:	Package
Title:	Nonparametric Quantile Density Estimation and Structural Analysis of First-Price Auctions
Version:	0.2.0
Description:	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.
License:	GPL (≥ 3)
URL:	https://github.com/DoostiH/qdfauction
BugReports:	https://github.com/DoostiH/qdfauction/issues
Encoding:	UTF-8
Depends:	R (≥ 4.1)
Imports:	stats, splines, utils, graphics, grDevices
Suggests:	testthat (≥ 3.1.7), knitr, rmarkdown
VignetteBuilder:	knitr
Config/testthat/edition:	3
LazyData:	true
Config/roxygen2/version:	8.1.1
NeedsCompilation:	no
Packaged:	2026-10-22 11:12:27 UTC; MQ20173444
Author:	Hassan Doosti [aut, cre, cph] (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>
Repository:	CRAN
Date/Publication:	2026-10-22 12:40:02 UTC

Cross-validation criteria for quantile density estimators

Description

Three data-driven criteria for the smoothing parameter of a quantile density estimator, written as functions of m = 1/h so that they can be passed to stats::optimize(). Each is evaluated on the grid u_i = i/n with spacings \Delta_i = X_{(i)} - X_{(i-1)}, X_{(0)} = 0, so that \sum_i \Delta_i\, \phi(\hat q(u_i)) approximates \int \phi\{1/\hat g(x)\}\,dx.

• bcv_criterion() is the biased (least-squares) cross-validation used by Chaubey, Dewan and Li (2024) and Doosti, Dewan and Talebian (2025):

  \mathrm{BCV}(h) = \sum_i \Delta_i\, \hat q(u_i;h)^{-2} - \frac{2}{n}\sum_i \hat q_{-i}(u_i;h)^{-1},

  i.e. the classical \int \hat g^2 - 2\,\bar{\hat g}_{-i} written in terms of \hat q = 1/\hat g. (Equation (11) of Doosti, Dewan and Talebian (2025) prints \hat q^2 in the first term; the simulation code, reproduced here, uses \hat q^{-2}.)

• rlcv_criterion() is the robust likelihood cross-validation of Wu (2019), -\{ \frac1n \sum_i \ln^*(\hat q_{-i}(u_i)^{-1}) - b^*(h) \}, with \ln^* linearised below the threshold a_n. It is written so that it is minimised.

• wbcv_criterion() is the weighted criterion of Doosti (2026b), which targets u\,q(u) (the quantity entering the first-price auction inversion) rather than q(u):

  \mathrm{WBCV}(h) = \sum_i \Delta_i\{u_i \hat q(u_i)\}^2 - \frac{2}{n}\sum_i u_i / \hat q_{-i}(u_i).

  Not recommended; kept only for backward compatibility. The criterion is not scale-invariant: its two terms have different units (the first scales like the cube of the data units, the second like their inverse), so the selected bandwidth depends on the units of the data; on data of order one or larger the first term dominates and the criterion tends to select the smoothest fit in m_range. The criterion is being revised (Doosti 2026b, revised version); wbcv_criterion() and bandwidth = "wbcv" issue a warning and are kept only so that existing code runs. Use BCV, RLCV or a numeric bandwidth instead.

Usage

bcv_criterion(m, x, fun, loo = TRUE, u_scheme = c("i/n", "i/(n+1)"))

rlcv_criterion(m, x, fun, loo = TRUE, u_scheme = c("i/n", "i/(n+1)"))

wbcv_criterion(m, x, fun, loo = TRUE, u_scheme = c("i/(n+1)", "i/n"))

Arguments

Value

a single numeric value; smaller is better.

References

Wu, X. (2019). Robust likelihood cross validation for kernel density estimation. Journal of Business & Economic Statistics, 37, 761–770.

See Also

select_bandwidth(), qdf()

Examples

x <- rgamma(60, 5, 1)
bcv_criterion(20, x, qdf_kernel)
rlcv_criterion(20, x, qdf_kernel)
optimize(bcv_criterion, c(10, 50), x = x, fun = qdf_bernstein)$minimum

Pseudo-maximum-likelihood estimation of an Archimedean copula from bids

Description

Transforms bids to pooled Hazen scores \hat u = \mathrm{rank}/(N+1), forms all within-auction pairs, and maximises the bivariate copula log-likelihood over \theta. Only pairs within the same auction are used (pairs straddling auctions are independent and would bias \hat\theta toward independence).

Usage

copula_fit(bids, family = c("clayton", "gumbel", "frank"), bounds = NULL)

Arguments

Value

a list with theta, tau (implied Kendall's tau), family, loglik and n_pairs.

Examples

set.seed(1)
U <- rcopula_archimedean(100, 3, "clayton", theta = 2)
copula_fit(U, "clayton")

Copula multiplier and diagonal functions of exchangeable Archimedean copulas

Description

For an exchangeable n-dimensional Archimedean copula C:

• copula_psi() is the copula multiplier of Doosti (2026a), \psi(u) = C_2(u,\ldots,u)/c(u,\ldots,u), which enters the inverse bid function v = b + \psi(u)\,q(u). Under independence \psi(u) = u/(n-1) and the GPV formula is recovered.

• copula_delta() is the diagonal \delta(u) = C(u,\ldots,u), the distribution function of the maximum of the n uniform scores.

• copula_delta_prime() is its derivative.

Usage

copula_psi(u, family = "independence", theta = NULL, n)

copula_delta(u, family = "independence", theta = NULL, n)

copula_delta_prime(u, family = "independence", theta = NULL, n)

Arguments

Value

numeric vector.

References

Doosti, H. (2026a). A copula-quantile density approach to affiliated private value auctions. Economics Letters, 261, 112837. doi:10.1016/j.econlet.2026.112837

Examples

u <- c(0.2, 0.5, 0.8)
copula_psi(u, "clayton", theta = 2, n = 3)
copula_psi(u, "independence", n = 3)      # u / (n - 1)
copula_delta(u, "gumbel", theta = 1.5, n = 3)

Nonparametric estimator of the copula multiplier

Description

Estimates the copula multiplier \psi(u) of the inverse bid function v = b + \psi(u)\,q(u) without a parametric copula. In the rank scale, with U a bidder's rank and M the highest rank among its rivals in the same auction,

\psi(u) = \frac{P(M \le u \mid U = u)}{f_{M \mid U}(u \mid u)},

the ratio of the conditional distribution and density of the highest rival bid at the bidder's own bid, expressed in ranks (the quantity identified by Li, Perrigne and Vuong 2002). Both are estimated with beta kernels on [0,1] (Chen 1999; Chen and Fan 2006):

\hat\psi(u) = \frac{\sum_i K_b(\hat U_i; u)\,1\{\hat M_i \le u\}} {\sum_i K_b(\hat U_i; u)\,K_b(\hat M_i; u)}, \qquad K_b(x; u) = \mathrm{Beta}(x;\, u/b + 1,\, (1-u)/b + 1).

Under independence \psi(u) = u/(n-1). This is the flexible copula multiplier of the copula–quantile density estimator (Doosti 2026a); it is used by fpa_values(family = "nonparametric") and by copula_spec_test().

Usage

copula_psi_np(bids, u, b = NULL)

Arguments

Value

numeric vector \hat\psi(u).

References

Chen, S. X. (1999). Beta kernel estimators for density functions. Computational Statistics & Data Analysis, 31, 131–145.

Chen, X. and Fan, Y. (2006). Estimation of copula-based semiparametric time series models. Journal of Econometrics, 130, 307–335.

Doosti, H. (2026a). A copula-quantile density approach to affiliated private value auctions. Economics Letters, 261, 112837. doi:10.1016/j.econlet.2026.112837

Examples

set.seed(1)
U <- rcopula_archimedean(300, 3, "clayton", theta = 2)
u <- c(0.2, 0.5, 0.8)
rbind(nonparametric = copula_psi_np(U, u),
      clayton       = copula_psi(u, "clayton", 2, 3),
      independence  = copula_psi(u, "independence", n = 3))

Specification test for a parametric copula of the private values

Description

Tests whether an exchangeable Archimedean copula family is compatible with the bids, using T = \sup_u |\hat\psi_{NP}(u) - \psi(u; \hat\theta)| over a grid of interior quantiles (Doosti 2026a), where \hat\psi_{NP} is copula_psi_np() and \hat\theta is the pseudo-maximum-likelihood estimate of copula_fit(). Critical values come from a parametric bootstrap under the null: rank matrices are simulated from the fitted copula, the parameter is re-estimated and the statistic recomputed. Because both multipliers depend on the bids only through their ranks, the test does not involve the marginal distribution.

Usage

copula_spec_test(
  bids,
  family = c("clayton", "gumbel", "frank", "independence"),
  B = 199,
  grid = seq(0.1, 0.9, by = 0.02),
  b = NULL
)

Arguments

Value

an object of class "htest" with the statistic, the bootstrap p-value and the fitted parameter.

Reproducibility

Call set.seed() beforehand to make the bootstrap reproducible.

Examples

set.seed(1)
U <- rcopula_archimedean(150, 3, "gumbel", theta = 2)
copula_spec_test(U, "gumbel", B = 99)
copula_spec_test(U, "independence", B = 99)

Convert between Kendall's tau and the copula parameter

Description

Convert between Kendall's tau and the copula parameter

Usage

copula_tau_to_theta(tau, family = c("clayton", "gumbel", "frank"))

copula_theta_to_tau(theta, family = c("clayton", "gumbel", "frank"))

Arguments

Value

numeric scalar.

Examples

copula_tau_to_theta(0.5, "clayton")   # 2
copula_theta_to_tau(2, "gumbel")      # 0.5

Quantile-based density estimator of Marmer and Shneyerov (2012)

Description

Estimates the private-value density in a symmetric IPV first-price auction directly from bids: the bid quantile function and density are estimated with a triweight kernel, the preliminary value quantile Q(\tau) + \tau/\{(n-1)\hat g(q(\tau))\} is monotonised around \tau_0 = 0.5, inverted to give \hat F(v), and the density is

\hat f(v) = \Big[\tfrac{n}{n-1}\hat g^{-1} - \tfrac{1}{n-1}\hat F(v)\,\hat g'/\hat g^3\Big]^{-1}

evaluated at q(\hat F(v)). Bandwidths follow the authors' normal-reference rules (1.06\,\hat\sigma\,n^{-1/5} for g, 1.06\,\hat\sigma\,n^{-1/7} for g'). This function is an R translation of the authors' MATLAB implementation, which is available from the first author's website (see inst/COPYRIGHTS for the exact location).

Usage

density_marmer_shneyerov(x, bids, n, tau0 = 0.5, cap = 1000)

Arguments

Value

numeric vector of density estimates at x.

References

Marmer, V. and Shneyerov, A. (2012). Quantile-based nonparametric inference for first-price auctions. Journal of Econometrics, 167(2), 345–357. doi:10.1016/j.jeconom.2011.09.020

Periodic discrete wavelet transform

Description

Forward (dwt_periodic()) and inverse (idwt_periodic()) periodised orthonormal DWT with L levels, as in Vidakovic (1999). length(x) must be divisible by 2^L. Coefficients are returned as c(smooth, detail_coarsest, ..., detail_finest). Translated from the MATLAB routines dwtr and Idwtr of B. Vidakovic.

Usage

dwt_periodic(x, L, filter = "coif1")

idwt_periodic(w, L, filter = "coif1")

Arguments

Value

numeric vector of the same length as x.

Examples

x <- rnorm(32)
all.equal(idwt_periodic(dwt_periodic(x, 3, "coif1"), 3, "coif1"), x)

Equilibrium bid function of a symmetric first-price auction

Description

Computes the symmetric equilibrium bid function in quantile space, \beta(u) = Q(u) - m(u) with the markup solving m'(u) = q(u) - m(u)/\psi(u), m(u_0) = 0 (Milgrom and Weber 1982; Doosti 2026a). Under independence \psi(u) = u/(n-1) and this is the Guerre–Perrigne–Vuong bid function \beta(v) = v - \int_0^v F(t)^{n-1}dt / F(v)^{n-1}. A reserve price at quantile p0 sets m(p_0) = 0. For CRRA bidders with utility U(t) = t^{1-\eta} the first-order condition is v = b + (1-\eta)\psi(u) q(u) (Guerre, Perrigne and Vuong 2009), so the markup solves the same ODE with \psi replaced by (1-\eta)\psi; under independence this gives Zincenko's (2024) risk-averse bid function \beta(v) = e_1 \int_0^v x F(x)^{e_2} f(x)\,dx / F(v)^{e_1}, e_1 = (n-1)/(1-\eta), e_2 = (n+\eta-2)/(1-\eta).

Usage

fpa_bid_function(
  Q,
  q = NULL,
  n,
  family = "independence",
  theta = NULL,
  p0 = 0,
  eta = 0,
  U = fpa_ugrid()
)

Arguments

Value

a list with U, bid (\beta(U)), value (Q(U)), markup and a function beta(v) interpolating the bid at value v.

Examples

bf <- fpa_bid_function(Q = function(u) qgamma(u, 2, 10),
                       q = function(u) 1 / dgamma(qgamma(u, 2, 10), 2, 10), n = 5)
bf$beta(c(0.1, 0.2, 0.5))
# compare with the direct integral form for IPV
v <- 0.2; v - integrate(function(t) pgamma(t, 2, 10)^4, 0, v)$value / pgamma(v, 2, 10)^4

Test for a corner solution of the optimal reserve under affiliation

Description

Decides whether any binding reserve price is optimal. The point rule classifies the sample as a corner (no binding reserve optimal) when

T = \min_{u \in [p_{lo},\, p_{hi}]} \hat\omega(u) - v_s \ge 0,

with \hat\omega the estimated affiliated virtual value of fpa_virtual_value(). The window starts at p_lo = 0.10 rather than at the boundary: with v_s equal to the lower end of the support, \omega(u) \to v_s as u \to 0 for every copula, so the region near zero carries no information about the corner, while the quantile density of the pseudo-values is least accurately estimated there.

The decision is the point rule on the full sample (decision = "point", the default and the rule used in Doosti 2026c): a corner is reported when T \ge 0. The null tested is "no reserve above the p_lo quantile beats no reserve". In applications the copula family should be chosen with copula_spec_test().

With B > 0, auctions are resampled with replacement, with the bandwidths of both stages selected once on the full sample and held fixed in every replication, and the frequency with which the point rule reports a corner is returned. Two alternative uses of the bootstrap are available but are not the default: decision = "bootstrap" reports a corner when that frequency is at least level; and corner_not_rejected records whether the upper 1 - \alpha bootstrap percentile of T is non-negative (a conservative rule, since T, a minimum of a noisy curve, is biased downward).

Usage

fpa_corner_test(
  bids,
  v_s = 0,
  family,
  method = "poisson",
  bandwidth = "rlcv",
  qdf_method = "poisson",
  p_lo = 0.1,
  p_hi = 0.98,
  B = 0,
  decision = c("point", "bootstrap"),
  level = 0.5,
  alpha = 0.1,
  ...
)

## S3 method for class 'fpa_corner_test'
print(x, ...)

Arguments

Value

An object of class "fpa_corner_test": a list with the statistic T, the decision corner, the estimated virtual value omega on the grid U, v_s, p_range, the bandwidths used in the two stages and the decision rule; with B > 0 also boot_stat, boot_corner_freq, boot_upper and corner_not_rejected. The print() method is called for its side effect of printing the decision and returns x invisibly.

Reproducibility

Call set.seed() beforehand to make the bootstrap reproducible.

References

Doosti, H. (2026c). Optimal reserve prices in affiliated private value auctions: a copula–quantile density approach. SSRN preprint. doi:10.2139/ssrn.7273869

Examples

set.seed(1)
Q <- function(u) (1 - 8 * u / 9)^(-1/2)
sim <- fpa_simulate(60, 3, Q, family = "clayton",
                    theta = copula_tau_to_theta(0.5, "clayton"))
fpa_corner_test(sim$bids, v_s = 1, family = "clayton")

Nonparametric estimation of the private-value density in first-price auctions

Description

Two-step estimation of the density f of independent private values. The first step recovers pseudo-values \hat v_i from the bids (by default with the Hickman–Hubbard boundary-corrected GPV inversion, or with any fpa_values() fit); the second step estimates the density of the pseudo-values by

• "wavelet_ll": linear wavelet estimator followed by local-linear smoothing (the best performer in Doosti, Dewan and Sbai 2026);

• "wavelet_linear", "wavelet_hard", "wavelet_block": linear, hard-thresholded and block-thresholded wavelet estimators;

• "wavelet_theory": the hard-thresholding estimator of Theorem 2 of Doosti, Dewan and Sbai (2026) on a periodised basis, with coefficients \hat\beta_{j,k} = N^{-1}\sum_i \psi_{j,k}(\Pi\hat v_i), coarsest level \tau = 3, finest level 2^{j_1} \approx \sigma_N^{-1} and level-dependent thresholds \eta_j = \kappa\,2^{j/2}\sigma_N, \sigma_N = N^{-2/5}(\log N)^{1/2}; by default \kappa is set so that the threshold at the finest level is the universal threshold \hat s\sqrt{2\log N} (\hat s: median absolute finest-level coefficient / 0.6745); pass kappa to override;

• "kde_hh": the boundary-corrected kernel estimator of Hickman and Hubbard (2015), i.e. GPV with boundary correction at both stages;

• "kde": Gaussian kernel with Sheather–Jones bandwidth;

• "empirical_quantile": finite-difference density from the empirical quantile function of the pseudo-values;

• "marmer_shneyerov": the quantile-based estimator of Marmer and Shneyerov (2012), computed directly from the bids (no first stage).

Because the wavelet basis lives on [0,1], bids and pseudo-values are mapped to the unit interval by a monotone map (scale): with "auto" the identity when the bids lie in [0,1] (pseudo-values outside are projected onto [0,1] for the wavelet coefficients), otherwise x \mapsto (\log x - a)/(b-a) ("log", which handles the heavy right tail of the GPV ratio) or a linear map ("minmax"). Densities are reported on the original scale through the Jacobian.

Usage

fpa_density(
  bids,
  n = NULL,
  method = c("wavelet_ll", "wavelet_linear", "wavelet_hard", "wavelet_block",
    "wavelet_theory", "kde_hh", "kde", "empirical_quantile", "marmer_shneyerov"),
  first_stage = "gpv_hh",
  x = NULL,
  j0 = 5L,
  h = 0.15,
  scale = c("auto", "log", "minmax", "none"),
  filter = "db3",
  dwt_filter = "coif1",
  levels = 3L,
  kernel = "triweight",
  side = c("lower", "both"),
  ...
)

Arguments

Value

An object of class "fpa_density": a list with x, f (density on the value scale), values (pseudo-values), values_u (on the unit scale), map, method, j0, h, first_stage, n, and fun, a function returning the density at arbitrary values.

References

Doosti, H., Dewan, I. and Sbai, E. (2026). Adaptive wavelet estimation of private-value densities in first-price auctions. SSRN preprint. doi:10.2139/ssrn.7618223

Hickman, B. R. and Hubbard, T. P. (2015). Journal of Applied Econometrics, 30, 739–762.

Marmer, V. and Shneyerov, A. (2012). Journal of Econometrics, 167, 345–357.

Examples

set.seed(1)
sim <- fpa_simulate(40, 5, Q = function(u) qbeta(u, 2, 2))
fd <- fpa_density(sim$bids, method = "wavelet_ll", j0 = 5, h = 0.1)
plot(fd, true = function(v) dbeta(v, 2, 2))
# timber auctions, three bidders, log scale
data(timber)
fd3 <- fpa_density(timber$n3, method = "wavelet_ll", j0 = 4, h = 0.15)
plot(fd3)

Bootstrap confidence intervals and bands for any private-value density estimator

Description

Pointwise percentile intervals and a uniform confidence band for the private-value density estimated by any fpa_density() method, for example the quantile-based estimator of Marmer and Shneyerov (2012) or the wavelet estimators. The whole two-step procedure (first stage, unit map, second stage) is repeated on B bootstrap samples. The uniform band is \hat f(x) \pm c^*\,\hat\sigma^*(x), with \hat\sigma^* the bootstrap standard deviation and c^* the 1-\alpha quantile of \sup_x |\hat f^*(x) - \hat f(x)|/\hat\sigma^*(x). For the GPV estimator, fpa_density_ci() gives the analytic studentisation of Ma, Marmer and Shneyerov (2019).

The percentile bootstrap reproduces the variability of the estimator but not its smoothing bias, so at the usual bandwidths the intervals can under-cover in moderate samples (in a small check with the Marmer–Shneyerov estimator, 240 bids and nominal level 0.95, pointwise coverage was about 0.86). Undersmoothing, i.e. a smaller bandwidth than is optimal for estimation, is the usual remedy.

Usage

fpa_density_boot(
  bids,
  n = NULL,
  method = "marmer_shneyerov",
  x = NULL,
  B = 199,
  level = 0.95,
  resample = c("bids", "auctions"),
  ...
)

## S3 method for class 'fpa_density_boot'
plot(x, true = NULL, ...)

Arguments

Value

an object of class "fpa_density_boot" with x, f, se, lower, upper (pointwise), band_lower, band_upper, crit, level, method, B and the matrix boot of bootstrap estimates.

Reproducibility

Call set.seed() beforehand to make the bootstrap reproducible.

Examples

set.seed(1)
sim <- fpa_simulate(60, 4, Q = function(u) qbeta(u, 2, 2))
bb <- fpa_density_boot(sim$bids, method = "marmer_shneyerov",
                       x = seq(0.15, 0.85, by = 0.05), B = 49)
bb
plot(bb, true = function(v) dbeta(v, 2, 2))

Confidence intervals and uniform confidence bands for the private-value density

Description

Inference for the two-step estimator of Guerre, Perrigne and Vuong (2000) following Ma, Marmer and Shneyerov (2019). The first stage uses the empirical distribution function of the bids and a fourth-order triweight kernel density estimate with bandwidth h_g = 3.72\,\hat\sigma_b\,n^{-1/5}; bids within h_g of the sample extremes are trimmed. The second stage is a triweight kernel density estimate of the pseudo-values with bandwidth h_f = 3.15\,\hat\sigma_v\,n_T^{-1/5}, where \hat\sigma_v and n_T are the standard deviation and number of the trimmed pseudo-values (Section 8 of the paper). The trimmed pseudo-values are those of bids at least h_g from the sample extremes. As scale estimate we use Silverman's robust \min(\hat\sigma, \mathrm{IQR}/1.349) rather than the standard deviation alone; the two coincide in the paper's designs, but the robust version protects the bandwidths against the few very large pseudo-values that arise where the estimated bid density is small.

Standard errors use the estimator (4.2) of the asymptotic variance, which accounts for the estimation error of the first stage. Pointwise intervals are percentile bootstrap intervals (Theorem 4.2; the default) or normal intervals \hat f \pm z_{1-\alpha/2}\,\mathrm{se}. The analytic standard error contains only the leading, first-stage term of the variance; in moderate samples, and where F(v) is small, the second-stage kernel variance is not negligible and normal intervals can under-cover, whereas the percentile intervals and the uniform band, whose critical value is bootstrapped, are not affected. The uniform band over grid is \hat f \pm \zeta^*\,\mathrm{se}, where \zeta^* is the 1-\alpha quantile of \sup_v |\hat f^*(v) - \hat f(v)|/\mathrm{se}(v) over bootstrap samples of the pooled bids (Theorem 5.2 and Corollary 5.1 of the paper).

The theory requires grid to lie in the interior of the support of the valuations. By default it spans the 5th to 95th percentiles of the pseudo-values of the bids between the 10th and 90th bid percentiles, which keeps it away from the long right tail of pseudo-values that the first stage produces where the bid density is small; with real data, choose the range with care, since the band is only valid where the density of values is bounded away from zero. Observations whose estimated bid density is not positive (possible with a fourth-order kernel near the boundary) are dropped.

Usage

fpa_density_ci(
  bids,
  n = NULL,
  grid = NULL,
  level = 0.95,
  B = 499,
  pointwise = c("percentile", "normal"),
  h_g = NULL,
  h_f = NULL,
  trim_estimator = FALSE,
  constants = c(3.72, 3.15),
  sd_v = c("trimmed", "all"),
  se = c("augmented", "asymptotic")
)

Arguments

Value

An object of class "fpa_density_ci": a list with v (grid), f (estimate), se, lower, upper (pointwise interval), band_lower, band_upper (uniform band), crit (\zeta^*), level, h_g, h_f, values (trimmed pseudo-values) and B.

Reproducibility

Fold assignment (or bootstrap resampling) uses R's random number generator; call set.seed() beforehand to make results exactly reproducible.

References

Ma, J., Marmer, V. and Shneyerov, A. (2019). Inference for first-price auctions with Guerre, Perrigne, and Vuong's estimator. Journal of Econometrics, 211, 507–538. doi:10.1016/j.jeconom.2019.02.006

Guerre, E., Perrigne, I. and Vuong, Q. (2000). Optimal nonparametric estimation of first-price auctions. Econometrica, 68, 525–574.

Examples

set.seed(1)
sim <- fpa_simulate(100, 5, Q = function(u) u)       # uniform values
ci  <- fpa_density_ci(sim$bids, grid = seq(0.2, 0.8, by = 0.01), B = 99)
ci
plot(ci, true = function(v) dunif(v))

Compare density estimators by held-out log-likelihood

Description

Outer folds-fold cross-validation of several second-stage estimators against a common target (the held-out pseudo-values from the first stage fitted on the training fold), as in Table 3 of Doosti, Dewan and Sbai (2026). Wavelet tuning parameters are fixed at j0, h.

Usage

fpa_density_cv(
  bids,
  n = NULL,
  methods = c("wavelet_ll", "kde_hh", "marmer_shneyerov"),
  folds = 5L,
  j0 = 5L,
  h = 0.15,
  first_stage = "gpv_hh",
  scale = "auto",
  kernel = "triweight",
  side = "lower",
  ...
)

Arguments

Value

a named numeric vector of summed held-out log-likelihoods.

Evaluate recovered values or an estimated density against the truth

Description

Performance criteria used in the papers behind this package.

fpa_evaluate_values() scores recovered private values (Doosti, Dewan and Talebian 2025): mise, the mean integrated squared error between a Gaussian kernel density (Sheather–Jones bandwidth) of the values and the true density; loglik, \sum_i \log f(\hat v_i); ks, the Kolmogorov–Smirnov distance between the empirical distribution of the values and F; and msep, the mean squared error against the true values when they are supplied (Doosti 2026a), with msep_trim excluding a share trim at each end of the true-value distribution.

fpa_evaluate_density() scores an fpa_density() object on its grid: mise over the whole grid and over the upper tail share of the support, and the expected log-likelihood \int f \log \hat f (Doosti, Dewan and Sbai 2026).

Usage

fpa_evaluate_values(values, pdf, cdf = NULL, true = NULL, trim = 0.05)

fpa_evaluate_density(density, pdf, tail = 0.1)

Arguments

Value

a named numeric vector.

Examples

set.seed(1)
sim <- fpa_simulate(20, 5, Q = function(u) qgamma(u, 10, 2))
fv  <- fpa_values(sim$bids, method = "bernstein", bandwidth = "bcv", monotone = "spline")
fpa_evaluate_values(fv, pdf = function(x) dgamma(x, 10, 2),
                    cdf = function(x) pgamma(x, 10, 2), true = sim$values)

Monte Carlo comparison of estimators

Description

Repeats: simulate auctions with fpa_simulate(), apply each estimator in estimators to the bids, and score it with score. Designed to regenerate the simulation tables of the papers behind this package.

Usage

fpa_montecarlo(
  R,
  L,
  n,
  Q,
  q = NULL,
  family = "independence",
  theta = NULL,
  estimators,
  score,
  seed = NULL,
  verbose = FALSE,
  p0 = 0,
  eta = 0
)

Arguments

Value

a data frame with columns rep, estimator and one column per criterion; summary() on the result (via aggregate) gives the table.

Examples

set.seed(1)
pdf <- function(x) dgamma(x, 10, 2); cdf <- function(x) pgamma(x, 10, 2)
mc <- fpa_montecarlo(R = 5, L = 20, n = 5, Q = function(u) qgamma(u, 10, 2),
  estimators = list(
    B_B  = function(b, v) fpa_values(b, method = "bernstein", bandwidth = "bcv"),
    GPV  = function(b, v) fpa_values(b, method = "gpv")),
  score = function(fit, b, v) fpa_evaluate_values(fit, pdf, cdf, v))
aggregate(cbind(mise, loglik, ks, msep) ~ estimator, mc, mean)

Optimal reserve price from recovered private values

Description

Maximises the plug-in revenue fpa_revenue() over a grid of reserve prices.

Usage

fpa_optimal_reserve(values, eta = NULL, v_s = 0, grid = NULL)

Arguments

Value

a list with r_star, revenue and the evaluated grid and R.

Plug-in estimator of the seller's expected revenue

Description

Estimates the seller's expected revenue R(r) in a symmetric first-price auction with reserve price r, from recovered private values (Zincenko 2024, eq. 6; Doosti 2026b, eq. 11). With L auctions of n bidders, pooled pseudo-values \hat v_1 \le \ldots \le \hat v_N, their empirical distribution \hat F, and the winner's value \check V_\ell = \max_i \hat v_{i\ell} in auction \ell,

\hat R(r) = v_s \hat F(r)^n + \frac1L\sum_{\ell=1}^L 1\{\check V_\ell > r\}\,\hat\beta(\check V_\ell; r),

\hat\beta(v; r) = \frac{r\,\hat F(r)^{e_1} + e_1 N^{-1}\sum_{r \le \hat v_j \le v} \hat v_j \hat F(\hat v_j)^{e_2}}{\hat F(v)^{e_1}},

with e_1 = (n-1)/(1-\eta), e_2 = (n+\eta-2)/(1-\eta) for CRRA bidders with parameter \eta (\eta = 0: risk neutral), and v_s the seller's own valuation.

Usage

fpa_revenue(values, r, eta = NULL, v_s = 0)

Arguments

Value

numeric vector \hat R(r).

References

Zincenko, F. (2024). Estimation and inference of seller's expected revenue in first-price auctions. Journal of Econometrics, 241(1), 105734. doi:10.1016/j.jeconom.2024.105734

Doosti, H. (2026b). Estimating seller's expected revenue in first-price sealed-bid auctions via quantile density functions. SSRN preprint. doi:10.2139/ssrn.7071380

Examples

set.seed(1)
sim <- fpa_simulate(100, 3, Q = function(u) 10 * u)      # values U[0, 10]
fv  <- fpa_values(sim$bids, method = "bernstein", bandwidth = "bcv")
r   <- 3:8
cbind(r, estimate = fpa_revenue(fv, r, v_s = 2),
      truth = fpa_revenue_model(function(u) 10 * u, n = 3, r = r, v_s = 2))

Bootstrap inference for the expected-revenue estimator

Description

Recomputes the pseudo-values and the plug-in revenue on B bootstrap samples of whole auctions (by default; see resample) (Zincenko 2024, Section 4.3; Doosti 2026b) and returns the bootstrap standard error \hat\sigma^*(r) = \{B^{-1}\sum_b (\hat R^*_b(r) - \hat R(r))^2\}^{1/2}, symmetric percentile-t pointwise confidence intervals (critical value: the 1-\alpha quantile of |Z^*_b(r)|), and a uniform confidence band over r based on the bootstrap distribution of \sup_r |Z^*_b(r)|, Z^*_b(r) = \{\hat R^*_b(r) - \hat R(r)\}/\hat\sigma^*(r). (The original simulation code used the 1-\alpha/2 quantile of Z^* for the pointwise intervals; the symmetric version guarantees that the band contains the intervals.)

Usage

fpa_revenue_boot(
  bids,
  r,
  B = 200,
  eta = 0,
  v_s = 0,
  resample = c("auctions", "bids"),
  alpha = c(0.1, 0.05, 0.01),
  trim = Inf,
  ...
)

Arguments

Value

an object of class "fpa_revenue_boot": a list with r, estimate, se, ci (array r by c(lower, upper) by alpha), band (same layout), the bootstrap critical values, the B by length(r) matrix of bootstrap estimates and, when the bandwidth was selected from the data, bandwidth: a list with the selected h and m, the selector, and at_boundary/boundary (see select_bandwidth()).

Reproducibility

Fold assignment (or bootstrap resampling) uses R's random number generator; call set.seed() beforehand to make results exactly reproducible.

Examples

set.seed(1)
sim <- fpa_simulate(60, 3, Q = function(u) 10 * u)
rb <- fpa_revenue_boot(sim$bids, r = c(3, 5, 7), B = 50, v_s = 2,
                       method = "bernstein", bandwidth = "sqrt")
rb

Expected revenue and optimal reserve price of a known model

Description

Computes the seller's expected revenue R(r) = v_s\,\delta(p) + \int_p^1 \beta(u; p)\,d\delta(u) for a private-value model with marginal quantile function Q, copula multiplier \psi and diagonal \delta (copula_psi(), copula_delta()), where \beta(\cdot; p) is the equilibrium bid with reserve price r = Q(p) (fpa_bid_function()). Under independence and risk neutrality this is the revenue used as the truth in Zincenko (2024). fpa_optimal_reserve_model() finds the interior maximiser p^* of R from the first-order condition \delta'(p)(v_s - Q(p)) + q(p)\int_p^1 e^{\Lambda(p)-\Lambda(u)}\delta'(u)\,du = 0.

Usage

fpa_revenue_model(
  Q,
  n,
  r = NULL,
  p = NULL,
  q = NULL,
  family = "independence",
  theta = NULL,
  v_s = 0,
  eta = 0,
  U = fpa_ugrid()
)

fpa_optimal_reserve_model(
  Q,
  n,
  q = NULL,
  family = "independence",
  theta = NULL,
  v_s = 0,
  eta = 0,
  p_lo = 1e-04,
  p_hi = 0.98,
  U = fpa_ugrid()
)

Arguments

Value

fpa_revenue_model(): numeric vector of expected revenues. fpa_optimal_reserve_model(): a list with p_star, r_star, revenue and interior.

Examples

Q <- function(u) (1 - 8 * u / 9)^(-1/2)             # truncated Pareto on [1, 3]
fpa_revenue_model(Q, n = 3, p = c(0.1, 0.3, 0.5), v_s = 1)
fpa_optimal_reserve_model(Q, n = 3, v_s = 1)
fpa_optimal_reserve_model(Q, n = 3, v_s = 1, family = "clayton", theta = 2)

Expected revenue and optimal reserve by the quantile-density functional

Description

Estimates the seller's expected revenue R(p) at reserve quantile p (reserve price r = Q_V(p)) and the optimal reserve from the revenue functional of Doosti (2026c, Proposition 1),

R(p) = v_s\,\delta(p) + \int_p^1 \beta(u; p)\,\delta'(u)\,du,

where \delta is the diagonal section of the copula of the private values (\delta(u) = u^n under independence) and \beta(u;p) is the equilibrium bid in quantile space with reserve Q_V(p). Writing K(w,u) = e^{\Lambda(w) - \Lambda(u)}, \Lambda' = 1/\psi, the proposition's \beta(u;p) = Q_V(u) - \int_p^u K(w,u) q_V(w)\,dw is equivalently

\beta(u; p) = Q_B(u) + \int_0^p K(w, u)\, q_V(w)\,dw,

because without a reserve the equilibrium bid quantile function is the observed bid quantile function Q_B. The estimator uses this second form: the empirical bid quantile function carries the upper tail, and the quantile density \hat q_V of the pseudo-values is needed only below the reserve quantile, where it is estimated reliably. The optimal reserve maximises \hat R over the quantile grid; at an interior optimum it satisfies the generalised Myerson condition r^* = v_s + q_V(p^*) H(p^*; \theta), which reduces to r^* = v_s + (1 - F(r^*))/f(r^*) under independence. This is Stage 3 of the copula–quantile density estimator; Stages 1–2 are fpa_values() with the same family.

Usage

fpa_revenue_qd(
  values,
  r = NULL,
  p = NULL,
  v_s = 0,
  qdf_method = "poisson",
  bandwidth = "rlcv",
  ...
)

fpa_optimal_reserve_qd(
  values,
  v_s = 0,
  qdf_method = "poisson",
  bandwidth = "rlcv",
  p_max = 0.98,
  ...
)

Arguments

Value

fpa_revenue_qd(): numeric vector \hat R. fpa_optimal_reserve_qd(): a list with r_star, p_star, revenue, interior (FALSE when \hat R is maximised at the smallest reserve, i.e. no binding reserve is optimal) and the evaluated curve p, R.

References

Doosti, H. (2026c). Optimal reserve prices in affiliated private value auctions: a copula–quantile density approach. SSRN preprint. doi:10.2139/ssrn.7273869

Examples

set.seed(1)
Q <- function(u) (1 - 8 * u / 9)^(-1/2)          # truncated Pareto on [1, 3]
th <- copula_tau_to_theta(0.25, "clayton")
sim <- fpa_simulate(50, 3, Q, family = "clayton", theta = th)
fv  <- fpa_values(sim$bids, method = "poisson", bandwidth = "rlcv", family = "clayton")
fpa_optimal_reserve_qd(fv, v_s = 1)[c("r_star", "p_star", "interior")]
fpa_optimal_reserve_model(Q, 3, family = "clayton", theta = th, v_s = 1)$r_star

Simulate first-price sealed-bid auctions

Description

Draws private values with a given marginal quantile function and an exchangeable Archimedean copula (independent private values by default), and computes exact equilibrium bids with fpa_bid_function().

Usage

fpa_simulate(
  L,
  n,
  Q,
  q = NULL,
  family = "independence",
  theta = NULL,
  p0 = 0,
  eta = 0,
  bid_function = NULL
)

Arguments

Value

a list with matrices values and bids (L by n) and the bid function used.

Examples

set.seed(1)
sim <- fpa_simulate(20, 5, Q = function(u) qlnorm(u, 0, 0.5))
str(sim$bids)

Quantile grid used for the equilibrium computations

Description

Three segments with refined tails, because 1/\psi(u) is stiff as u \to 0 for every family and as u \to 1 for Gumbel.

Usage

fpa_ugrid(n_interior = 2001L)

Arguments

Value

sorted numeric vector in (0, 1).

Recover private values from first-price auction bids

Description

Nonparametric inversion of the equilibrium bid function of a symmetric first-price sealed-bid auction through the quantile density of bids:

\hat v = b + \lambda^{-1}\{\psi(\hat u)\,\hat q(\hat u)\},\qquad \hat u = \hat G(b),

where \hat q is a quantile density estimate (qdf()), \psi is the copula multiplier (equal to u/(n-1) under independent private values, so that \psi q = G/\{(n-1)g\} and the formula is that of Guerre, Perrigne and Vuong 2000), and \lambda^{-1} accounts for risk aversion (Guerre, Perrigne and Vuong 2009): the identity for risk-neutral bidders and y \mapsto (1-\eta) y for CRRA utility U(t) = t^{1-\eta}.

method = "zincenko" gives the kernel estimator of Zincenko (2024): a triweight kernel for \hat G and \hat g with normal-reference bandwidths, and linear extrapolation of the inverse bid function within h_g^{0.99} of the boundaries instead of trimming; the implementation is adapted from code provided by F. Zincenko. method = "gpv" gives the conventional benchmark \hat v = b + \hat G(b)/\{(n-1)\hat g(b)\} with a Gaussian kernel and Sheather–Jones bandwidth for \hat g; "gpv_bc" renormalises a triweight kernel by its mass inside the bid support (a simple boundary correction; the Hickman–Hubbard correction is provided by fpa_density()).

Usage

fpa_values(
  bids,
  n = NULL,
  method = "indirect_poisson",
  bandwidth = "bcv",
  eta = 0,
  family = "independence",
  theta = NULL,
  u_scheme = c("i/(n+1)", "i/n"),
  monotone = c("none", "spline", "isotonic"),
  trim = 0.1,
  replace = c("all", "boundary"),
  df = 6,
  psi_bw = NULL,
  ...
)

Arguments

Value

An object of class "fpa_values": a list with

bids, u

sorted pooled bids and their scores

q, psi, markup

the quantile density, the copula multiplier and the estimated markup \lambda^{-1}(\psi q) at each bid

values_raw, values

pseudo-values before and after the monotonicity step (identical when monotone = "none")

values_mat

when bids is a matrix, values rearranged to the original L by n layout

qdf

the fitted qdf() object (or NULL for the kernel benchmarks)

copula

the copula specification / fit

Monotonicity

The equilibrium bid function is increasing, so \hat v should be increasing in b. Departures occur mostly near the boundaries. monotone = "spline" follows Doosti, Dewan and Talebian (2025): a natural cubic spline of \hat v on b is fitted to the central 1 - 2\,\mathrm{trim} share of the bids and its fitted values replace \hat v either everywhere (replace = "all", the published implementation) or only in the trimmed tails (replace = "boundary"). monotone = "isotonic" applies the pool-adjacent-violators rearrangement (stats::isoreg()). "none" returns the raw pseudo-values.

References

Guerre, E., Perrigne, I. and Vuong, Q. (2000). Optimal nonparametric estimation of first-price auctions. Econometrica, 68, 525–574.

Guerre, E., Perrigne, I. and Vuong, Q. (2009). Nonparametric identification of risk aversion in first-price auctions under exclusion restrictions. Econometrica, 77, 1193–1227.

Doosti, H., Dewan, I. and Talebian, M. (2025). Economics Letters, 257, 112670.

Doosti, H. (2026a). Economics Letters, 261, 112837.

Examples

set.seed(1)
sim <- fpa_simulate(20, 5, Q = function(u) qgamma(u, 2, 10))
fit <- fpa_values(sim$bids, method = "bernstein", bandwidth = "bcv")
fit
plot(fit, true = sim$values)
# risk-averse bidders with CRRA eta = 0.25
fit_ra <- fpa_values(sim$bids, method = "bernstein", bandwidth = 0.05, eta = 0.25)
# affiliated values, Clayton copula estimated from the data

sim2 <- fpa_simulate(50, 3, Q = function(u) 1 + 2 * u^2, family = "clayton", theta = 2)
fit2 <- fpa_values(sim2$bids, method = "indirect_poisson", family = "clayton")
fit2$copula$theta

Affiliated virtual value

Description

The virtual value of affiliated private values in quantile space,

\omega(u; \theta) = Q_V(u) - q_V(u)\, H(u; \theta), \qquad H(p) = \frac{1}{\delta'(p)} \int_p^1 K(p, u)\, \delta'(u)\, du,

with K(w, u) = e^{\Lambda(w) - \Lambda(u)}, \Lambda' = 1/\psi and \delta the diagonal section of the copula (Doosti 2026c). The derivative of expected revenue is R'(p) = \delta'(p)\{v_s - \omega(p)\}, so no binding reserve is optimal (a corner) when \omega(u) \ge v_s for all u; under independence \omega is the Myerson virtual value v - (1 - F(v))/f(v).

fpa_virtual_value_model() evaluates \omega for a known model; fpa_virtual_value() estimates it from an fpa_values() object, with the quantile function and quantile density of the pseudo-values estimated as in fpa_revenue_qd().

Usage

fpa_virtual_value_model(
  Q,
  q,
  n,
  family = "independence",
  theta = NULL,
  U = fpa_ugrid()
)

fpa_virtual_value(values, qdf_method = "poisson", bandwidth = "rlcv", ...)

Arguments

Value

A list with the grid U, the virtual value omega and the factor H; fpa_virtual_value() also returns the estimated Q and q on U and the bandwidth h used for the pseudo-values.

References

Doosti, H. (2026c). Optimal reserve prices in affiliated private value auctions: a copula–quantile density approach. SSRN preprint. doi:10.2139/ssrn.7273869

Examples

Q <- function(u) (1 - 8 * u / 9)^(-1/2)              # truncated Pareto on [1, 3]
q <- function(u) (4 / 9) * (1 - 8 * u / 9)^(-3/2)
vv <- fpa_virtual_value_model(Q, q, n = 3, family = "clayton", theta = 2)
range(vv$omega)

Boundary-corrected kernel density estimator of Hickman and Hubbard (2015)

Description

Kernel density estimator with the transformation-based boundary correction of Karunamuni and Zhang (2008) at the lower endpoint of the support, as implemented by Hickman and Hubbard (2015) for first-price auctions (their kspdf_bc; this function is an R translation of that code, whose authors ask that Hickman and Hubbard (2015) be cited). The bandwidth is Silverman's rule scaled by Haerdle's (1991) canonical-kernel constant. The estimate is set to zero outside the range of the data. With side = "both" the same correction is applied at the upper endpoint through the reflected sample.

Usage

kde_hh(
  x,
  data,
  kernel = "triweight",
  side = c("lower", "both"),
  hardle = TRUE,
  h = NULL
)

Arguments

Value

a numeric vector with attribute "h" (the bandwidth used).

References

Hickman, B. R. and Hubbard, T. P. (2015). Replacing sample trimming with boundary correction in nonparametric estimation of first-price auctions. Journal of Applied Econometrics, 30, 739–762. doi:10.1002/jae.2385

Karunamuni, R. J. and Zhang, S. (2008). Some improvements on a boundary corrected kernel density estimator. Statistics & Probability Letters, 78, 499–507.

Examples

x <- rbeta(200, 2, 1)
g <- seq(0.01, 0.99, by = 0.01)
plot(g, kde_hh(g, x), type = "l"); lines(g, dbeta(g, 2, 1), lty = 2)

Local-linear smoother with Gaussian kernel

Description

Post-smoothing of a wavelet estimate on a grid (Ramirez and Vidakovic 2010; Fan 1992): for each x solves the weighted least-squares line through ⁠(grid, y)⁠ with weights K((grid - x)/h) and returns its value at x. Translated from the MATLAB routine loc_lin of B. Vidakovic.

Usage

loclin_smooth(x, grid, y, h)

Arguments

Value

numeric vector.

Construct and check a kernel for the kernel-type estimators

Description

make_kernel() turns a kernel density function into the object used by qdf_kernel(), qdf_kernel_corrected(), qdf_jones(), qdf_soni() and qdf() (argument kernel). The distribution function needed by the boundary-corrected and Soni estimators is computed numerically. check_kernel() reports the properties that matter for these estimators: total mass, symmetry, support, order (the first non-vanishing moment), \int k^2 and whether the kernel takes negative values; it warns when a property required by the theory fails.

Usage

make_kernel(k, name = "custom", support = 1, n_grid = 20001L)

check_kernel(kernel, verbose = TRUE)

Arguments

Value

make_kernel(): an object of class "qdf_kernel" (a list with the density d, the distribution function p, name and the check results). check_kernel(): a list of properties, printed invisibly.

Examples

# the biweight (quartic) kernel
biweight <- make_kernel(function(u) 15 / 16 * (1 - u^2)^2 * (abs(u) <= 1), "biweight")
biweight
x <- rgamma(100, 5)
qdf_kernel(c(0.25, 0.5, 0.75), x, h = 0.05, kernel = biweight)
check_kernel("epanechnikov")

Evaluate scaling functions \phi_{j,k}(z) = 2^{j/2}\phi(2^j z - k)

Description

Evaluate scaling functions \phi_{j,k}(z) = 2^{j/2}\phi(2^j z - k)

Usage

phi_jk(z, j, k, filter = "db3", levels = 10L)

Arguments

Value

numeric vector or matrix of values.

Examples

z <- seq(0, 1, by = 0.01)
plot(z, phi_jk(z, 3, 2), type = "l")

Plot an estimated private-value density

Description

Plot an estimated private-value density

Usage

## S3 method for class 'fpa_density'
plot(x, true = NULL, rug = TRUE, ...)

Arguments

Value

No return value, called for its side effect of drawing the estimated density (with an optional rug of pseudo-values and the true density). The object x is returned invisibly.

Plot a GPV density estimate with confidence bands

Description

Plot a GPV density estimate with confidence bands

Usage

## S3 method for class 'fpa_density_ci'
plot(x, true = NULL, ...)

Arguments

Value

No return value, called for its side effect of drawing the density estimate with its pointwise confidence interval and uniform confidence band. The object x is returned invisibly.

Plot a revenue curve with bootstrap bands

Description

Plot a revenue curve with bootstrap bands

Usage

## S3 method for class 'fpa_revenue_boot'
plot(x, alpha = 0.05, ...)

Arguments

Value

No return value, called for its side effect of drawing the estimated expected-revenue curve with its pointwise confidence interval (dashed) and uniform confidence band (shaded). The object x is returned invisibly.

Plot recovered private values against bids

Description

Plot recovered private values against bids

Usage

## S3 method for class 'fpa_values'
plot(x, true = NULL, ...)

Arguments

Value

No return value, called for its side effect of plotting the recovered pseudo-values against the bids (with the monotone fit and the true values when available). The object x is returned invisibly.

Plot a quantile density estimate

Description

Plot a quantile density estimate

Usage

## S3 method for class 'qdf'
plot(x, true = NULL, ...)

Arguments

Value

No return value, called for its side effect of drawing the estimated quantile density (and the true one, if supplied). The object x is returned invisibly.

Predict from a fitted quantile density estimate

Description

Predict from a fitted quantile density estimate

Usage

## S3 method for class 'qdf'
predict(object, u = NULL, ...)

Arguments

Value

numeric vector of \hat q(u).

Mother wavelet and periodised wavelet basis on the unit interval

Description

psi_jk() evaluates the mother wavelet \psi_{j,k}(z) = 2^{j/2}\psi(2^j z - k), with \psi obtained from the scaling function by the two-scale relation \psi(x) = \sqrt 2 \sum_k g_k \phi(2x - k), g_k = (-1)^k h_{N-k}. phi_per() and psi_per() evaluate the periodised functions \sum_l \phi_{j,k}(z + l) and \sum_l \psi_{j,k}(z + l), which for 2^j \ge N form an orthonormal basis of L_2([0,1]) (Daubechies 1992, Section 9.3); they are used by the nonlinear estimators qdf_wavelet_nl() and fpa_density(method = "wavelet_theory").

Usage

psi_jk(z, j, k, filter = "db3")

phi_per(z, j, k, filter = "db3")

psi_per(z, j, k, filter = "db3")

Arguments

Value

numeric vector or matrix.

Examples

z <- seq(0, 1, length.out = 2001)
P <- psi_per(z, 4, 0:15, "db3")
round(crossprod(P)[1:3, 1:3] / 2000, 3)     # approximately the identity

Nonparametric estimation of a quantile density function

Description

Estimates the quantile density function q(u) = Q'(u) = 1/g(Q(u)) (Parzen 1979; also called the sparsity function, Tukey 1965) from an i.i.d. sample, with the smoothing parameter chosen by cross-validation. Ten estimators are available; see qdf_kernel(), qdf_poisson(), qdf_jones() and qdf_wavelet() for their definitions and the simulation evidence in Doosti, Dewan and Talebian (2025), where the indirect Poisson estimator with BCV bandwidth performed best for first-price auction inversion.

Usage

qdf(
  x,
  method = c("indirect_poisson", "kernel", "kernel_corrected", "poisson", "bernstein",
    "jones", "soni", "wavelet", "wavelet_hard", "wavelet_block"),
  bandwidth = c("bcv", "rlcv", "wbcv", "sqrt", "cv"),
  m_range = NULL,
  u = NULL,
  H = NULL,
  kernel = "gaussian",
  j0 = NULL,
  loo = NULL,
  grid = NULL,
  u_scheme = NULL,
  optimizer = NULL,
  rescale = TRUE,
  ...
)

Arguments

Value

An object of class "qdf": a list with components

x

the sorted sample

u, q

the evaluation grid and the estimated quantile density on it

Q

the empirical quantile function on u (for convenience)

method, h, m, H

the estimator and its smoothing parameters

selection

the output of select_bandwidth(), or NULL when a numeric bandwidth was supplied

with methods predict.qdf(), print() and plot.qdf().

References

Doosti, H., Dewan, I. and Talebian, M. (2025). Nonparametric estimation of private value distributions in first-price auctions: Evaluating quantile density function approaches. Economics Letters, 257, 112670. doi:10.1016/j.econlet.2025.112670

Parzen, E. (1979). Nonparametric statistical data modeling. Journal of the American Statistical Association, 74, 105–121.

Examples

set.seed(1)
x <- rgamma(80, 5, 1)
fit <- qdf(x, method = "bernstein", bandwidth = "bcv")
fit
predict(fit, u = c(0.1, 0.5, 0.9))
# compare with the truth 1 / dgamma(qgamma(u, 5), 5)
plot(fit, true = function(u) 1 / dgamma(qgamma(u, 5, 1), 5, 1))

Indirect estimators of the quantile density

Description

Estimators that exploit q(u) = 1 / g(Q(u)) by first estimating the density g.

• qdf_jones() (Jones 1992; estimator 5) is \hat q(u) = 1/\hat g_h(Q_n(u)) with a Gaussian kernel density estimator \hat g_h.

• qdf_soni() (Soni, Dewan and Jain 2012; estimator 6) double-smooths Jones' estimator on the probability scale. With form = "integrated" (the form printed in Doosti, Dewan and Talebian 2025),

  \hat q(u) = \sum_{i=1}^n \frac{K((i/n - u)/H) - K(((i-1)/n - u)/H)}{\hat g_h(X_{(i)})},

  where K is the kernel's distribution function; with form = "riemann" (the original implementation of Soni, Dewan and Jain)

  \hat q(u) = \frac{1}{nH}\sum_{i=1}^n \frac{k((i/n - u)/H)}{\hat g_h(X_{(i)})}.

  Here h is the density bandwidth on the data scale and H the smoothing bandwidth on the probability scale. For right-censored data supply status; the empirical scores i/n are then replaced by the Kaplan–Meier estimate \hat F(X_{(i)}), as in Soni et al.

• qdf_indirect_poisson() (Chaubey, Dewan and Li 2024; estimator 7) smooths the empirical distribution function with Poisson weights, inverts the result numerically to obtain a smooth quantile estimator, and takes the reciprocal of the implied density at that quantile. It requires positive data.

Usage

qdf_jones(u, x, h, kernel = "gaussian")

qdf_soni(
  u,
  x,
  h,
  H,
  kernel = "gaussian",
  form = c("integrated", "riemann"),
  status = NULL
)

qdf_indirect_poisson(
  u,
  x,
  h,
  tol = .Machine$double.eps^0.25,
  invert = c("auto", "newton", "uniroot")
)

Arguments

Value

numeric vector of quantile density estimates at u.

References

Soni, P., Dewan, I. and Jain, K. (2012). Nonparametric estimation of quantile density function. Computational Statistics & Data Analysis, 56, 3876–3886.

Examples

x <- rgamma(100, 5, 1)
u <- c(0.25, 0.5, 0.75)
qdf_jones(u, x, h = 0.5)
qdf_soni(u, x, h = 0.5, H = 0.05)
qdf_indirect_poisson(u, x, h = 0.05)

Direct kernel estimators of the quantile density

Description

Kernel-type estimators that smooth the empirical quantile function directly.

• qdf_kernel() is the Jones (1992) estimator

  \hat q(u) = \sum_{i=1}^n X_{(i)} \{ k_h((i-1)/n - u) - k_h(i/n - u) \}

  with a Gaussian kernel k_h(a) = k(a/h)/h.

• qdf_kernel_corrected() is the boundary-corrected version (estimator 2 of Doosti, Dewan and Talebian 2025), which renormalises the kernel weights by the mass falling inside [0,1] and adds a correction proportional to the kernel quantile estimator.

Usage

qdf_kernel(u, x, h, kernel = "gaussian")

qdf_kernel_corrected(u, x, h, kernel = "gaussian")

Arguments

Value

numeric vector of quantile density estimates at u.

References

Jones, M. C. (1992). Estimating densities, quantiles, quantile densities and density quantiles. Annals of the Institute of Statistical Mathematics, 44, 721–727.

Chaubey, Y. P., Dewan, I. and Li, J. (2024). On some non parametric estimators of the quantile density function for a stationary associated process. Communications in Statistics – Theory and Methods, 53, 5553–5573.

See Also

qdf() for automatic bandwidth selection.

Examples

x <- rgamma(100, 5, 1)
u <- seq(0.05, 0.95, by = 0.05)
qdf_kernel(u, x, h = 0.05)
qdf_kernel_corrected(u, x, h = 0.05)

Series estimators of the quantile density

Description

Estimators that smooth the empirical quantile function Q_n with probability weights on a lattice k/m, m = \lfloor 1/h \rfloor.

• qdf_poisson() uses Poisson weights (Chaubey, Dewan and Li 2021; estimator 3 of Doosti, Dewan and Talebian 2025):

  \hat q(u) = m \sum_{k=0}^{m} Q_n(k/m)\, w_k'(m u)

  where w_k(\mu) = p_k(\mu)/P_m(\mu) are truncated Poisson weights.

• qdf_bernstein() uses Bernstein polynomials (estimator 4):

  \hat q(u) = m \sum_{k=0}^{m-1} \{Q_n((k+1)/m) - Q_n(k/m)\} b_k(u; m-1).

Both require non-negative data because Q_n(0) is taken to be 0; qdf_bernstein() can instead use Q_n(0) = X_{(1)} (zero_at_origin = FALSE), which is preferable when the data do not start near zero (for example bids in dollars).

qdf_bernstein_loo() returns the exact leave-one-out Bernstein estimates \hat q_{-i}(u_i), i = 1, \dots, n, used by the cross-validation criteria: removing X_{(i)} shifts every higher order statistic down by one position, so the leave-one-out lattice values are obtained by an index shift in O(nK) operations, without refitting.

Usage

qdf_poisson(u, x, h)

qdf_bernstein(u, x, h, zero_at_origin = TRUE)

qdf_bernstein_loo(u, x, h, zero_at_origin = TRUE)

Arguments

Value

numeric vector of quantile density estimates at u; for qdf_bernstein_loo(), the vector (\hat q_{-1}(u_1), \dots, \hat q_{-n}(u_n)), where u has the length of x and is paired with the sorted sample.

References

Chaubey, Y. P., Dewan, I. and Li, J. (2021). On some smooth estimators of the quantile function for a stationary associated process. Sankhya B, 83 (Suppl 1), 114–139. doi:10.1007/s13571-020-00242-x

Examples

x <- rgamma(100, 5, 1)
qdf_poisson(c(0.25, 0.5, 0.75), x, h = 0.05)
qdf_bernstein(c(0.25, 0.5, 0.75), x, h = 0.05)
# exact leave-one-out estimates at u_i = i/n agree with refitting
xs <- sort(x); u <- seq_along(xs) / length(xs)
loo <- qdf_bernstein_loo(u, xs, h = 0.05)
all.equal(loo[10], qdf_bernstein(u[10], xs[-10], h = 0.05))

Wavelet estimators of the quantile density

Description

Projection estimators of q(u) on a wavelet basis of [0,1] (Chesneau, Dewan and Doosti 2016; Shirazi and Doosti 2022). The scaling coefficients are estimated by the warped-basis formula

\hat c_{j_0,k} = \int \phi_{j_0,k}(\hat F(x))\,dx = \sum_{i=1}^{n-1} (X_{(i+1)} - X_{(i)})\,\phi_{j_0,k}(i/n),

which is exact for the empirical distribution function \hat F (integration = "exact"); "simpson" reproduces the composite Simpson rule of the original MATLAB implementation.

Three estimators are available through threshold:

• "none": the linear estimator \hat g_L = \sum_k \hat c_{j_0,k}\phi_{j_0,k};

• "hard": term-by-term hard thresholding of a levels-level periodic DWT of the coefficient vector;

• "block": Cai's BlockJS block thresholding (Shirazi and Doosti 2022).

With smooth = TRUE the estimate is post-smoothed by the local-linear smoother of Ramirez and Vidakovic (2010) with bandwidth h; this "smoothed" version was the best performer in both papers' simulations.

Usage

qdf_wavelet(
  u,
  x,
  j0 = 5L,
  h = 0.15,
  threshold = c("none", "hard", "block"),
  smooth = TRUE,
  filter = "db3",
  dwt_filter = "coif1",
  levels = 3L,
  integration = c("exact", "simpson"),
  ...
)

Arguments

Value

numeric vector of quantile density estimates at u.

References

Chesneau, C., Dewan, I. and Doosti, H. (2016). Nonparametric estimation of a quantile density function by wavelet methods. Computational Statistics & Data Analysis, 94, 161–174.

Shirazi, E. and Doosti, H. (2022). Nonparametric estimation of a quantile density function under L_p risk via block thresholding method. Communications in Statistics – Simulation and Computation, 51, 539–553.

Ramirez, P. and Vidakovic, B. (2010). Wavelet density estimation for stratified size-biased sample. Journal of Statistical Planning and Inference, 140, 419–432.

Examples

set.seed(1)
x <- rbeta(500, 0.5, 0.5)
u <- seq(0.02, 0.98, by = 0.02)
q_true <- 1 / dbeta(qbeta(u, 0.5, 0.5), 0.5, 0.5)
q_lin  <- qdf_wavelet(u, x, j0 = 5, smooth = FALSE)
q_blk  <- qdf_wavelet(u, x, j0 = 5, threshold = "block", smooth = TRUE)
plot(u, q_true, type = "l"); lines(u, q_lin, col = 2); lines(u, q_blk, col = 4)

Nonlinear wavelet estimators of the quantile density with theoretical thresholds

Description

Hard-thresholding and block-thresholding estimators of the quantile density built on the wavelet coefficients of the quantile density itself,

\hat c_{\tau,k} = \int \phi_{\tau,k}(\hat F(x))\,dx, \qquad \hat d_{j,k} = \int \psi_{j,k}(\hat F(x))\,dx,

computed exactly for the empirical distribution function, on a periodised orthonormal basis of [0,1]:

\hat q(u) = \sum_k \hat c_{\tau,k}\phi_{\tau,k}(u) + \sum_{j=\tau}^{j_1} \sum_k \hat d_{j,k}\, \mathrm{keep}_{j,k}\, \psi_{j,k}(u).

• rule = "hard_general": hard thresholding with \lambda_j = K 2^{3j/2}\sqrt{2 p \ln n / n}, K = \sup|\psi'| and 2^{j_1} \approx (n/\ln n)^{1/4} (Chesneau, Dewan and Doosti 2016, Theorem 3.2, no smoothness condition beyond the Besov ball);

• rule = "hard_lipschitz": hard thresholding with \lambda_j = \kappa\sqrt{\ln n / n} and 2^{j_1} \approx \sqrt{n/\ln n} (Theorem 3.4, under a Lipschitz-1/2 condition on q);

• rule = "block": block thresholding (Shirazi and Doosti 2022, eq. 6): at each level the coefficients are grouped in blocks of length l = \lfloor\ln n\rfloor and a block is kept when l^{-1}\sum_{j \in B}|\hat d_{j,k}|^p > c\, n^{-p/2}, with j_1 = \lfloor\log_2(n / l^2)\rfloor.

The constants of the theorems are not available in practice. By default \kappa = \hat s\sqrt{2n}, so that \lambda = \hat s\sqrt{2\ln n} (the universal threshold), and c = 4.505^{p/2}\hat s^p n^{p/2}, so that a block is kept when its mean |\hat d|^p exceeds 4.505^{p/2}\hat s^p (the BlockJS level of Cai 1999), where \hat s is the median absolute coefficient at the finest level divided by 0.6745. With smooth = TRUE the estimate is post-smoothed by local linear regression (Ramirez and Vidakovic 2010), the "smoothed" versions of both papers.

The periodised basis replaces the boundary-corrected basis of Cohen et al. (1993) assumed by the theory; periodisation identifies the endpoints of [0,1], so near u = 0 and u = 1 these estimators should be read with care. Local-linear post-smoothing (smooth = TRUE) helps when q is small at both ends of [0,1], but when q is large at an endpoint it spreads the error caused by periodisation into the interior, and the unsmoothed estimators are then more accurate. In simulations the cross-validated estimators of qdf() were more accurate than all of these rules; the estimators here are mainly intended for studying the theory.

Usage

qdf_wavelet_nl(
  u,
  x,
  rule = c("hard_lipschitz", "hard_general", "block"),
  tau = 3L,
  j1 = NULL,
  const = NULL,
  p = 2,
  smooth = FALSE,
  h = 0.15,
  filter = "db3"
)

Arguments

Value

numeric vector of estimates at u, with attribute "kept", the number of detail coefficients retained at each level.

References

Chesneau, C., Dewan, I. and Doosti, H. (2016). Computational Statistics & Data Analysis, 94, 161–174.

Shirazi, E. and Doosti, H. (2022). Communications in Statistics – Simulation and Computation, 51, 539–553.

Examples

set.seed(1)
x <- rbeta(500, 0.5, 0.5)
u <- seq(0.05, 0.95, by = 0.05)
q_true <- 1 / dbeta(qbeta(u, 0.5, 0.5), 0.5, 0.5)
cbind(q_true,
      lipschitz = qdf_wavelet_nl(u, x, "hard_lipschitz", smooth = TRUE),
      block     = qdf_wavelet_nl(u, x, "block", smooth = TRUE))

Smoothed quantile function implied by the indirect Poisson estimator

Description

Returns the Poisson-smoothed quantile function \tilde Q(u) used by qdf_indirect_poisson(); useful for plotting or for constructing pseudo-values.

Usage

quantile_indirect_poisson(
  u,
  x,
  h,
  tol = .Machine$double.eps^0.25,
  invert = c("auto", "newton", "uniroot")
)

Arguments

Value

numeric vector of smoothed quantiles at u.

Simulate exchangeable Archimedean copula scores

Description

Marshall–Olkin frailty samplers in base R.

Usage

rcopula_archimedean(L, n, family = "independence", theta = NULL)

Arguments

Value

an L by n matrix with uniform margins.

Examples

U <- rcopula_archimedean(500, 2, "frank", theta = 5)
cor(U, method = "kendall")[1, 2]        # about 0.46

Tabulate the scaling function by the Daubechies–Lagarias algorithm

Description

Evaluates \phi on the dyadic grid k/2^{L} of its support [0, N-1] (N = filter length) by the Daubechies–Lagarias product formula (Vidakovic 1999, Section 3.4), and caches the result. phi_jk() interpolates from this table. Translated from the MATLAB routine Phijk of B. Vidakovic (Ramirez and Vidakovic 2010).

Usage

scaling_function(filter = "db3", levels = 10L, extra = 20L)

Arguments

Value

a list with x and phi.

Select the smoothing parameter of a quantile density estimator

Description

Minimises one of the criteria in bcv_criterion() over m = 1/h in m_range with stats::optimize(), optionally after a coarse grid search.

Usage

select_bandwidth(
  x,
  fun,
  selector = c("bcv", "rlcv", "wbcv"),
  m_range = c(10, 50),
  loo = NULL,
  grid = NULL,
  u_scheme = NULL,
  optimizer = NULL
)

Arguments

Value

a list with h, m, selector, criterion (the minimised value) and m_range; at_boundary (logical) and boundary ("lower", "upper" or NA), which record a minimiser at an end of m_range; with grid = "exhaustive" also the evaluated grid grid_m and criterion values grid_cv.

Boundary solutions

If the minimiser lies at either end of m_range, whatever the search method, the criterion did not select a bandwidth in that range. A warning is issued and at_boundary = TRUE is returned; widen m_range or give the bandwidth directly.

Examples

x <- rgamma(60, 5, 1)
select_bandwidth(x, qdf_poisson, "bcv", m_range = c(10, 50))

Cross-validated choice of the wavelet resolution level and bandwidth

Description

Chooses j0 (and h for "wavelet_ll") by folds-fold cross-validation on the bids: the first stage and the unit map are fitted on the training bids and applied to the held-out bids, and the second-stage density \hat f_{-k} is scored on the held-out pseudo-values by one of two criteria:

• "lscv" (default): least-squares cross-validation (Rudemo 1982; Bowman 1984), \sum_k \{ \int \hat f_{-k}^2 - 2\,\bar{\hat f}_{-k}(\text{held-out}) \}, an unbiased estimate of the integrated squared error up to a constant, so that the selected pair targets MISE;

• "loglik": the held-out log-likelihood \sum_k \sum_i \log \hat f_{-k}(\hat v_i), which targets Kullback–Leibler divergence. Because the held-out points are pseudo-values carrying first-stage error, this criterion tends to favour the smallest bandwidth on the grid; it is kept for comparison.

Both criteria are evaluated on the interior [\delta, 1-\delta] of the unit interval (trim): pseudo-values that fall outside [0,1] are projected onto the boundary (Algorithm 1 of the paper), which creates a spurious point mass there that a small bandwidth reproduces; excluding a boundary strip from the score removes this artefact, in the spirit of the boundary trimming of Guerre, Perrigne and Vuong (2000). For "lscv" the trimmed criterion is \int_\delta^{1-\delta}\hat f^2 - 2 n_k^{-1}\sum_{\hat v_i \in [\delta,1-\delta]} \hat f(\hat v_i), an unbiased estimate of the ISE over the interior up to a constant.

The returned score is oriented so that larger is better in both cases (-LSCV for "lscv").

Usage

select_fpa_density(
  bids,
  n,
  method = "wavelet_ll",
  first_stage = "gpv_hh",
  j0_grid = 3:6,
  h_grid = c(0.03, 0.05, 0.08, 0.12, 0.2, 0.3, 0.5),
  folds = 5L,
  criterion = c("lscv", "loglik"),
  scale = "auto",
  filter = "db3",
  dwt_filter = "coif1",
  levels = 3L,
  kernel = "triweight",
  side = "lower",
  n_int = 2001L,
  trim = 0.05
)

Arguments

Value

a list with j0, h, criterion and the matrix of summed scores ll (rows j0_grid, columns h_grid; larger is better).

Reproducibility

Fold assignment (or bootstrap resampling) uses R's random number generator; call set.seed() beforehand to make results exactly reproducible.

References

Rudemo, M. (1982). Empirical choice of histograms and kernel density estimators. Scandinavian Journal of Statistics, 9, 65–78.

Bowman, A. W. (1984). An alternative method of cross-validation for the smoothing of density estimates. Biometrika, 71, 353–360.

Cross-validated choice of (j0, h) for the wavelet estimator

Description

Chooses the resolution level and the post-smoothing bandwidth of qdf_wavelet() by folds-fold cross-validation. The implied density of the data is \hat g(x) = 1/\hat q(\hat G_{\rm train}(x)). With criterion = "lscv" (default) the pair minimises the least-squares criterion \int \hat g^2 - 2 n_k^{-1} \sum \hat g(x_i) over held-out observations, using \int \hat g(x)^2 dx = \int_0^1 1/\hat q(u)\,du; this targets integrated squared error (Rudemo 1982; Bowman 1984). With criterion = "loglik" it maximises the held-out log-likelihood. Pairs with h below the lattice spacing 1/(2^{j_0}-1) are not considered, since the local-linear smoother is then ill-conditioned.

Usage

select_wavelet(
  x,
  j0_grid = 3:6,
  h_grid = c(0.03, 0.05, 0.08, 0.12, 0.2, 0.3),
  folds = 5L,
  criterion = c("lscv", "loglik"),
  ...
)

Arguments

Value

a list with j0, h, and the matrix of criterion values (ll, rows j0_grid, columns h_grid).

Reproducibility

Fold assignment (or bootstrap resampling) uses R's random number generator; call set.seed() beforehand to make results exactly reproducible.

Examples

set.seed(2)
x <- rbeta(300, 0.5, 0.5)
select_wavelet(x, j0_grid = 4:6, h_grid = c(0.05, 0.15), folds = 3)

Threshold wavelet coefficients

Description

Applies a thresholding rule to the detail coefficients of a periodic DWT.

• "hard": term-by-term hard thresholding, \hat d\,1\{|\hat d| > \lambda\}, with \lambda = c\,\hat\sigma\sqrt{\log(n)/n} (Chesneau, Dewan and Doosti 2016; c = 8 in their implementation).

• "block": the BlockJS rule of Cai (1999) used by Shirazi and Doosti (2022): at each level the coefficients are grouped in blocks of length L = \lfloor \log n \rfloor; a block is kept if \sum_{j \in B} \hat d_j^2 \ge \lambda^* L \hat\sigma^2 with \lambda^* = 4.50524 (rule = "truncate"), or shrunk by (1 - \lambda^* L \hat\sigma^2 / S_B^2)_+ (rule = "james-stein").

\hat\sigma is the MAD of the finest-level coefficients.

Usage

threshold_coefficients(
  w,
  L,
  n,
  type = c("hard", "block"),
  const = NULL,
  block_length = NULL,
  rule = c("truncate", "james-stein")
)

Arguments

Value

thresholded coefficient vector.

References

Cai, T. T. (1999). Adaptive wavelet estimation: a block thresholding and oracle inequality approach. Annals of Statistics, 27, 898–924.

USFS timber sealed-bid auctions (homogenised bids)

Description

Homogenised per-unit bids (dollars per unit of timber) in the sealed-bid subsample of the U.S. Forest Service timber auction data assembled by Lu and Perrigne (2008) and deposited in the Journal of Applied Econometrics Data Archive, split by the number of bidders. The bids are not the raw bids: observed lot heterogeneity has been removed as described in Details. The announced reserve price is treated as non-binding in the literature using these data.

Usage

timber

Format

A list with two numeric matrices, one row per auction and one column per bidder, in the order in which the archive files record them:

n2

107 auctions with 2 bidders (columns bid1, bid2)

n3

108 auctions with 3 bidders (columns bid1, bid2, bid3)

Details

Each auction in the archive reports the per-unit bid of every bidder together with two lot characteristics: the appraised per-unit value and the timber volume. Observed heterogeneity in these characteristics is removed before the structural analysis, as is standard for these data (Lu and Perrigne 2008; Haile, Hong and Shum 2003), so that the methods of this package can be applied to bids drawn from a common distribution within each stratum.

Within each stratum (n = 2 and n = 3 separately), the log bid is regressed by ordinary least squares on X = (1, \log(\mathrm{appraisal}), \log(\mathrm{volume})), which is constant within an auction, and every bid is rescaled to the average lot,

\tilde b = b \exp(\bar X' \hat\beta - X' \hat\beta).

The intercept cancels in the difference, so only the slope coefficients matter. The regressions fit the log bids with R^2 = 0.671 for n = 2 (slopes 0.642 and 0.043) and R^2 = 0.554 for n = 3 (slopes 0.553 and 0.070). The stored values are rounded to six decimal places.

The script that rebuilds the object from the archive files is data-raw/timber.R of the package sources; the archive files themselves are not redistributed here.

Source

Journal of Applied Econometrics Data Archive, replication files of Lu, J. and Perrigne, I. (2008). Estimating risk aversion from ascending and sealed-bid auctions: the case of timber auction data. Journal of Applied Econometrics, 23, 871–896. doi:10.1002/jae.1032; data archive entry doi:10.15456/jae.2022319.0720356598. Please cite that paper when using the data.

References

Haile, P. A., Hong, H. and Shum, M. (2003). Nonparametric tests for common values at first-price sealed-bid auctions. Cowles Foundation Discussion Paper 1445.

Examples

data(timber)
fit <- fpa_values(timber$n3, method = "bernstein", bandwidth = "bcv")
summary(fit$values - fit$bids)     # implied markups

Orthonormal wavelet filters

Description

Returns the scaling (low-pass) filter of a compactly supported orthonormal wavelet. Available: "haar", "db2", "db3", "db4" (Daubechies with 2–4 vanishing moments), "sym4" (Symmlet 8-tap), "coif1", "coif2" (Coiflets). A numeric vector is returned unchanged after checking that it sums to \sqrt 2.

Usage

wavelet_filter(filter = "db3")

Arguments

Value

numeric filter coefficients.

Examples

wavelet_filter("db3")