A Nonparametric Examination of Capital-Skill Complementarity

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, but the strength of the evidence depends upon the definition of skilled labor and the elasticity of substitution measure being used. The added flexibility of the nonparametric procedure is also able to uncover that the elasticities of substitution vary across countries, groups of countries and time periods.capital-skill complementarity, elasticity of substitution, nonparametric kernel, stochastic dominance

A Test for Multimodality of Regression Derivatives with an Application to Nonparametric Growth Regressions

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 examine the finite sample properties of the test along with those from a calibrated version of it which corrects for the conservative nature of Silverman-type tests. The test is included in a study on nonparametric growth regressions. The results show that in the estimation of unconditional β-convergence, the distribution of the parameter estimates is multimodal with one mode in the negative region (primarily OECD economies) and possibly two modes in the positive region (primarily non-OECD economies) of the parameter estimates. The results for conditional β-convergence show that the density is predominantly negative and unimodal. Finally, the application attempts to determine why particular observations posess positive marginal effects on initial income in both the unconditional and conditional frameworks.Nonparametric Kernel; Convergence; Modality Tests

Are We Wasting Our Children's Time by Giving Them More Homework?

Following an identification strategy that allows us to largely eliminate unobserved student and teacher traits, we examine the effect of homework on math, science, English and history test scores for eighth grade students in the United States. Noting that failure to control for these effects yields selection biases on the estimated effect of homework, we find that math homework has a large and statistically meaningful effect on math test scores throughout our sample. However, additional homework in science, English and history are shown to have little to no impact on their respective test scores.first differencing, unobserved traits, instrumental variable, selection bias, homework

Canonical Higher-Order Kernels for Density Derivative Estimation

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 visual comparison of kernel estimates.Derivative Estimation, AMISE

Normal Reference Bandwidths for the General Order, Multivariate Kernel Density Derivative Estimator

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 literature.Derivative Estimation, Smoothing, AMISE

Imposing Economic Constraints in Nonparametric Regression: Survey, Implementation and Extension

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 available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.identification, concavity, Hessian, constraint weighted bootstrapping, earnings function

The Dog that Finally Barked:England as an Emerging Political Community

This report presents evidence which suggests the emergence of a new kind of Anglo-British identity in which the English component is increasingly the primary source of attachment for English people. It also suggests that English identity is becoming more politicised: that is, the more English a person feels, the more likely they are to believe that the current structure of the UK is unfair and to support a particularly English dimension to the governance of England

Modular generalized Springer correspondence: an overview

This is an overview of our series of papers on the modular generalized Springer correspondence. It is an expansion of a lecture given by the second author in the Fifth Conference of the Tsinghua Sanya International Mathematics Forum, Sanya, December 2014, as part of the Master Lecture `Algebraic Groups and their Representations' Workshop honouring G. Lusztig. The material that has not appeared in print before includes some discussion of the motivating idea of modular character sheaves, and heuristic remarks about geometric functors of parabolic induction and restriction.Comment: 19 pages. Version 2 includes more examples and tables in Section

Modular generalized Springer correspondence II: classical groups

We construct a modular generalized Springer correspondence for any classical group, by generalizing to the modular setting various results of Lusztig in the case of characteristic-$0$ coefficients. We determine the cuspidal pairs in all classical types, and compute the correspondence explicitly for $\mathrm{SL}(n)$ with coefficients of arbitrary characteristic and for $\mathrm{SO}(n)$ and $\mathrm{Sp}(2n)$ with characteristic-$2$ coefficients.Comment: 52 pages. Version 2 corrects a minor mistake in the combinatorics of the type D case; no numbered statements are affected. Version 3 has minor additions, mostly in Section 2; final version, to appear in J. Eur. Math. So

Heterogeneity in Schooling Rates of Return

This paper relaxes the assumption of homogeneous rates of return to schooling by employing nonparametric kernel regression. This approach allows us to examine the differences in rates of return to education both across and within groups. Similar to previous studies we find that on average blacks have higher returns to education than whites, natives have higher returns than immigrants and younger workers have higher returns than older workers. Contrary to previous studies we find that the average gap of the rate of return between white and black workers is larger than previously thought and the gap is smaller between immigrants and natives. We also uncover significant heterogeneity, the extent of which differs both across and within groups. The estimated densities of returns vary across groups and time periods and are often skewed. For example, during the period 1950-1990, at least 5% of whites have negative returns. Finally, we uncover the characteristics common amongst those with the smallest and largest returns to education. For example, we find that immigrants, aged 50-59, are most likely to have rates of return in the bottom 5% of the population.nonparametric, Mincer regressions, rate of return to education