In this note we present r th order kernel density derivative estimators using canonical higher-order kernels. These canonical rescalings uncouple the choice of kernel and scale factor. This approach is useful for selection of the order of the kernel in a data-driven procedure as well as for...

This note derives the general form of the approximate mean integrated squared error for the q-variate, th-order kernel density r th derivative estimator. This formula allows for normal reference rule-of-thumb bandwidths to be derived. We give tables for some of the most common cases in the...

Bayesian model averaging has increasingly witnessed applications across an array of empirical contexts. However, the dearth of available statistical software which allows one to engage in a model averaging exercise is limited. It is common for consumers of these methods to develop their own...

This paper describes the relative merits and attractiveness of the newest Bayesian model averaging package, BMS, available in the statistical software R to implement a Bayesian model averaging exercise. This package provides the user with a wide range of customizable priors for conducting a BMA...

In this paper we propose an asymptotically equivalent single-step alternative to the two-step partially linear model estimator in Robinson (1988). The estimator not only has the potential to decrease computing time dramatically, it shows substantial finite sample gains in Monte Carlo simulations.

Recent research documents a hump in the trade diversification-income relationship. We show that when time variation is accounted for that the appearance of this hump is much less obvious.

We adopt a counterfactual approach to decompose labor productivity growth into growth of Technological Productivity (TEP), growth of the capital-labor ratio and growth of Total Factor Productivity (TFP). We bring the decomposition to the data using international countrysectoral information...

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...

There has recently been a dramatic increase in the number of papers that have combined quasi-experimental methods with hedonic property models. This is largely due to the concern that cross-sectional hedonic methods may be severely biased by omitted variables. While the empirical literature has...