likelihood in that they do not require complete specification of the error processes. Instead they offer a nonparametric …

We propose a nonparametric test of conditional independence based on the empirical distribution function. The …

allow for covariates to enter the unit root (or near unit root) model in a nonparametric fashion, so that our model is an …

We propose a nonparametric empirical distribution function based test of an hypothesis of conditional independence …

The nonparametric censored regression model is y = max[c, m(x) + e], where both the regression function m(x) and the … the derivatives of an uncensored nonparametric regression. We then estimate the regression function itself by solving the … usual estimators in uncensored nonparametric regression. We also provide root n estimates of weighted average derivatives of …

nonparametric treatment of regression errors is permitted so that it is not necessary to be explicit about the dynamic specification …

a nonparametric short memory component and using a reduced rank regression with only a single lag, standard information …

nonparametric short memory components and shifting volatility provided the penalty coefficient C_{n}-> infinity and C_{n}/n -> 0 as …

nonparametrically using instrumental variables. First, we derive the minimax optimal sup-norm convergence rates for nonparametric … functionals in nonparametric demand estimation when prices, and possibly incomes, are endogenous. The second is to establish the … functionals of h_0. Both applications provide new and useful tools for empirical research on nonparametric models with endogeneity. …