Brent R. Hickman and Timothy P. Hubbard, "Replacing Sample Trimming with Boundary Correction in Nonparametric Estimation of First-Price Auctions", Journal of Applied Econometrics, Vol. 30, No. 5, 2015, pp. 739-762. All data we used are already in the Journal of Applied Econometrics Data Archive, because we revisited the application considered in Sandra Campo, Isabelle M. Perrigne, and Quang H. Vuong, "Asymmetry in First-Price Auctions With Affiliated Private Values", Journal of Applied Econometrics, Vol. 18, No. 2, 2003, pp. 179-207. See: http://qed.econ.queensu.ca/jae/2003-v18.2/campo-perrigne-vuong/ In addition, we have provided three sets of replication files coded in Matlab. These are stored in three zip files. Below we describe the files in each zip file. Briefly, the files in "MC-related.zip" can be used to replicate the Monte Carlo experiments presented in our paper; the files in "Application-related.zip" can be used to replicate the application of the proposed estimator to the OCS data noted above; the files in "Extensions.zip" are the core files we think a user interested in applying boundary correction to a new environment and/or application might want to start with. Note that the programs can be used to execute the boundary-corrected approach we propose as well as a trimming-based approach. We ask researchers who use our code, in whatever capacity, to please cite this paper. All the Matlab files are ASCII files in DOS format. Unix/Linux users should use "unzip -a". Description of files in "MC-related.zip" hh_mcstudy.m: main driver file to run and replicate Monte Carlo experiments computeise.m: compute integrated squared error between kernel-smoothed density estimate and true density computeise_stg1.m: compute integrated squared error between kernel-smoothed density estimate and true density using a grid of points for truth computeise_stg1_postrun.m: variation of computeise.m called by hh_mcstudy.m computeise_stg2_postrun.m: variation of computeise.m called by hh_mcstudy.m evalkspdf.m: evaluate specified kernel at a grid of points evalskpdf_num.m: evaluate function of a specified kernel at a grid of points evalkspdf_denom.m: evaluate function of a specified kernel at a grid of points kscdf.m: compute empirical distribution function kspdf_bc.m: compute boundary-corrected kernel-smoothed estimate of density function kspdf_no_bc.m: compute kernel-smoothed estimate of a density function mixbetarnd.m: function to help generate (pseudo) random valuations from a mixture of Beta distributions processoutput.m: takes as input the beta_mc_ex#.mat (where # = {1,2,3,4}) files which are output from hh_mcstudy.m to replicate results presented in the paper risamint_mixbetas.m: function for computing the integral portion of the symmetric IPVP bid function trim.m: trim bids within a bandwidth of the minimum and maximum observed bids Description of files in "Application-related.zip" Remember to download the OCS data from Campo, Perrigne, and Vuong as described above before running these programs which read in a file called Ocs702.dat. bcappliedtoCPV.m: boundary correction applied to the CPV data bcappliedtoCPVfcn: a function called by compare_figs.m which implements boundary correction on the CPV data compare_figs.m: a file which compares the boundary correction and trimming-based approaches evalkspdf.m: evaluate specified kernel at a grid of points evalskpdf_num.m: evaluate function of a specified kernel at a grid of points evalkspdf_denom.m: evaluate function of a specified kernel at a grid of points jointDistbcfcn.m: compute boundary-corrected kernel-smoothed estimate of joint density function kscdf.m: compute empirical distribution function kscdf2dCPV.m: compute two-dimensional empirical distribution function using standard kernel kscdf2dCPVbc.m: compute two-dimensional empirical distribution function using boundary-corrected kernel kspdf_bc.m: compute boundary-corrected kernel smoothed estimate of density function kspdf_no_bc_CPV.m: compute kernel-smoothed estimate of a density function kspdf2dCPV.m: compute two-dimensional kernel-smoothed estimate of a density function kspdf2dCPV.m: compute two-dimensional kernel-smoothed estimate of a density function using boundary correction kspdf2dCPVFAST_joint.m: compute two-dimensional kernel-smoothed estimate of a density function using boundary correction in a faster way kzroutine.m: KZ routine for boundary corrected kernel kzker_joint.m: another KZ routine for boundary corrected kernel replicateCPV.m: a replication file of the trimming-based approach of Campo, Perrigne, and Vuong replicateCPVfcn.m: a function called by compare_figs.m which implements trimming on the CPV data trim_comparison_scatter.m: a program to compare the different ways of trimming trimasymmetricCPV.m: trim auctions for asymmetric APV case trimsymmetricCPV.m: trim auctions for symmetric APV case Description of files in "Extensions.zip" bcgpv_driver.m: generates uniformly distributed data and applies boundary correction evalkspdf.m: evaluate specified kernel at a grid of points evalskpdf_num.m: evaluate function of a specified kernel at a grid of points evalkspdf_denom.m: evaluate function of a specified kernel at a grid of points kscdf.m: compute empirical distribution function kspdf_bc.m: compute boundary-corrected kernel-smoothed estimate of density function