Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparamteric...

We apply recent results from the statistics literature to test for multimodality of worldwide distributions of several (unweighted and population-weighted) measures of labor productivity. Specifically, we employ Silverman (Bump) and Dip modality tests, calibrated to correct for their incorrect...

A simple graphical approach to presenting results from nonlinear regression models is described. In the face of multiple covariates, ‘partial mean’ plots may be unattractive. The approach here is portable to a variety of settings and can be tailored to the specific application at hand. A...

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparametric...

This paper takes a fresh look at the impact of financial development on economic growth by using recently developed kernel methods that allow for heterogeneity in partial effects, nonlinearities and endogenous regressors. Our results suggest that while the positive impact of financial...

Uncovering gradients is of crucial importance across a broad range of economic environments. Here we consider data-driven bandwidth selection based on the gradient of an unknown regression function. The procedure developed here is automatic and does not require initial estimation of unknown...