We propose a complete framework for model-free difference-in-differences analysis with covariates, where model-free means data-driven, in particular nonparametric estimation and testing, variable and scale choice. We start with searching for the preferred data setup by simultaneously choosing...

Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the...

This paper uses nonparametric 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 investigates how students' collegiate athletic participation affects their subsequent labor market success. It uses newly developed distributional tests to establish that the wage distribution of former college athletes is significantly different from non-athletes and that athletic...

In this paper we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient and free from the curse of dimensionality. Monte...

In this paper, we employ a partially linear nonparametric additive regression estimator, with recent U.S. Current Population Survey data, to analyze returns to schooling. Similar to previous research, we find that blacks and Hispanics have higher rates of return on average. However, for married...

We examine the (potentially nonlinear) relationship between inequality and growth using a method which does not require an a priori assumption on the underlying functional form. This approach reveals a plateau completely missed by commonly used (nonlinear) parametric approaches - the economy...

In this article we overview nonparametric (spline and kernel) regression methods and illustrate how they may be used in labor economic applications. We focus our attention on issues commonly found in the labor literature such as how to account for endogeneity via instrumental variables in a...

As epidemiological studies have shown that conditions during gestation and early childhood affect adult health outcomes, we examine the effect of local labor market conditions in the year of birth on cognitive development in childhood. To address the endogeneity of labor market conditions, we...

We examine the educational production function and efficiency of public school districts in Illinois. Using nonparametric kernel methods, we find that most traditional schooling inputs are irrelevant in determining test scores (even in a very general setting). Property tax caps are the only...