This paper proposes plug-in bandwidth selection for kernel density estimation with discrete data via minimization of mean summed square error. Simulation results show that the plug-in bandwidths perform well, relative to cross-validated bandwidths, in non-uniform designs. We further find that...

The distributional specifications for the composite regression error term most often used in Stochastic Frontier Analysis (SFA) are inherently bounded as regards their skewness and excess kurtosis coefficients. These bounds provide simple diagnostic tools and model selection/rejection criteria...

This chapter surveys nonparametric methods for estimation and inference in a panel data setting. Methods surveyed include profile likelihood, kernel smoothers, as well as series and sieve estimators. The practical application of nonparametric panel-based techniques is less prevalent that, say,...

The hat matrix maps the vector of response values in a regression to its predicted counterpart. The trace of this hat matrix is the workhorse for calculating the effective number of parameters in both parametric and nonparametric regression settings. Drawing on the regression literature, the...

We consider shape constrained kernel-based probability density function (PDF) and probability mass function (PMF) estimation. Our approach is of widespread potential applicability and includes, separately or simultaneously, constraints on the PDF (PMF) function itself, its integral (sum), and...

This paper compares a nonparametric generalized least squares (NPGLS) estimator to parametric feasible GLS (FGLS) and variants of heteroscedasticity robust standard error estimators (HRSEs) in an applied setting. Given myriad alternative HRSEs, a clear consensus on which version to use does not...

This work describes a versatile and readily-deployable sensitivity analysis of an ordinary least squares (OLS) inference with respect to possible endogeneity in the explanatory variables of the usual k-variate linear multiple regression model. This sensitivity analysis is based on a derivation...