This paper uses non-parametric kernel methods to construct observation-specific elasticities of substitution for a balanced panel of 73 developed and developing countries to examine the capital-skill complementarity hypothesis. The exercise shows some support for capital-skill complementarity,...

This paper presents a method to test for multimodality of an estimated kernel density of derivative estimates from a nonparametric regression. The test is included in a study of nonparametric growth regressions. The results show that in the estimation of unconditional β-convergence the...

Monotonicity of the equilibrium bidding strategy is a key property of structural auction models. Traditional nonparametric estimators provide a flexible means of uncovering salient features of auction data, but do not formally impose the monotonicity assumption that is inherent in the models...

This paper presents a method to test for multimodality of an estimated kernel density of parameter estimates from a local-linear least-squares regression derivative. The procedure is laid out in seven simple steps and a suggestion for implementation is proposed. A Monte Carlo exercise is used to...

In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a semiparametric partially linear fixed effects model. To determine whether a...

Using nonparametric, production-frontier methods, we decompose labor productivity growth into components attributable to technological change (shifts in the world production frontier), technological catch-up (movements toward or away from the frontier), and physical and human capital...

Utilizing parametric and nonparametric techniques, we assess the role of a heretofore relatively unexplored "input" in the educational process, homework, on academic achievement. Our results indicate that homework is an important determinant of student test scores. Relative to more standard...