In this paper, we propose nonparametric estimators of sharp bounds on the distribution of treatment effects of a binary treatment and establish their asymptotic distributions. We note the possible failure of the standard bootstrap with the same sample size and apply the fewer-than-<italic>n</italic> bootstrap to...

This paper develops a wavelet (spectral) approach to testing the presence of a unit root in a stochastic process. The wavelet approach is appealing, since it is based directly on the different behavior of the spectra of a unit root process and that of a short memory stationary process. By...

Let <italic>F</italic> denote a distribution function defined on the probability space (Ω,null,<italic>P</italic>), which is absolutely continuous with respect to the Lebesgue measure in <italic>R</italic> with probability density function <italic>f</italic>. Let <italic>f</italic>₀(·,β) be a parametric density function that depends on an unknown <italic>p</italic> × 1 vector β. In this...

In this paper we develop nonparametric estimators of the joint time series data generating process (DGP) of (<italic>x</italic>, <italic>y</italic>) at different <italic>t</italic>-values, of conditional DGP, of the conditional mean of <italic>x</italic> given the past values of <italic>x</italic> and <italic>y</italic>, and, more generally, the conditional mean of (<italic>x</italic>, <italic>y</italic>) given their past values...

In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R²) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being...