This note establishes that the fully nonparametric classical errors-in-variables model is identifiable from data on the regressor and the dependent variable alone, unless the specification is a member of a very specific parametric family. This family includes the linear specification with...

A new uniform expansion is introduced for sums of weighted kernel-based regression residuals from nonparametric or semiparametric models. This result is useful for deriving asymptotic properties of semiparametric estimators and test statistics with data-dependent bandwidth, random trimming, and...

We consider estimation of means of functions that are scaled by an unknown density, or equivalently, integrals of conditional expectations. The "ordered data" estimator we provide is root n consistent, asymptotically normal, and is numerically extremely simple, involving little more than...

Let r(x,z) be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses identification and consistent estimation of the unknown functions H, M, G and F, where r(x, z) = H[M (x, z)] and M(x,z) = G(x) + F(z). An estimation algorithm is...

Existing studies have explored either only one or two of the mechanisms that human capital externalities percolate at only macrogeographic levels. This paper, by using the 1990 Massachusetts census data, tests four mechanisms at the microgeographic levels in the Boston metropolitan area labor...

This paper investigates the presence of fractal dynamics in stock returns. We improve upon existing literature in two ways: i) instead of rescaled-range analysis, we use the more efficient semi- nonparametric procedure suggested by Geweke and Porter-Hudak (GPH, 1983), and ii) to ensure...