The local polynomial estimator is particularly affected by the curse of dimensionality. So, the potentialities of such a tool become ineffective for large dimensional applications. Motivated by this, we propose a new estimation procedure based on the local linear estimator and a nonlinearity...

On normal days, the temperature decreases with altitude, allowing air pollutants to rise and disperse. During inversion episodes, a warmer air layer at higher altitude traps pollutants close to the ground. We show how readily available NASA satellite data on vertical temperature profiles can be...

After two decades of academic debate on the social capital-growth nexus, discussion still remains open. Most of the literature so far, however, has followed the one-size-its-all approach, neglecting that the great disparities across geographical units might have implications in this...

This paper uses panel data and the Local Linear Kernel Estimator (LLKE) to investigate the effects of aid on economic growth in developing countries. Specifically, we investigate the robustness of a popular parametric specification of the aid/economic growth re lationship in Less Developed...

This paper uses panel data and the Local Linear Kernel Estimator (LLKE) to investigate the effects of aid on economic growth in developing countries. Specifically, we investigate the robustness of a popular parametric specification of the aid/economic growth re lationship in Less Developed...

The aim of this paper is to provide simple nonparametric methods to estimate finitemixture models from data with repeated measurements. Three measurements suffice for the mixture to be fully identified and so our approach can be used even with very short panel data. We provide distribution...

We focus on the construction of confidence corridors for multivariate nonparametric generalized quantile regression functions. This construction is based on asymptotic results for the maximal deviation between a suitable nonparametric estimator and the true function of interest which follow...

This paper studies nonparametric identification in market level demand models for differentiated products. We generalize common models by allowing for the distribution of heterogeneity parameters (random coefficients) to have a nonparametric distribution across the population and give conditions...

This paper employs numerical simulations of the Park and Sabourian (2011) herd model to derive new theory-based predictions for how information risk and market stress influence aggregate herding intensity. We test these predictions empirically using a comprehensive data set of highfrequency and...