Using the 1999 durable consumption survey data in rural China, we examine the importance of local spillovers in the diffusion of three major durable goods, i.e., color television set, washing machine, and refrigerator. We find that, with control for many family characteristics, a household is...

Purpose – The effects of social learning and network externalities in the diffusion of a new product imply that there should be local spillovers from existing owners to new adopters in a closely-related community. Using the 1999 durable consumption survey data in rural China, this paper...

Prior studies on the debt-equity choice of firms focus on capital market oriented economies. This paper examines whether firms in Japan, the world’s largest bank-oriented economy, adjust their debt-equity choice towards the target. We find that the leverage ratios of Japanese firms do adjust...

In this paper we propose a new nonparametric kernel based estimator for a density function $f$ which achieves bias reduction relative to the classical Rosenblatt-Parzen estimator. Contrary to some existing estimators that provide for bias reduction, our estimator has a full asymptotic...

In this paper, we investigate the finite sample performance of four kernel-based estimators that are currently available for additive non-parametric regression models—the classic backfitting estimator (CBE), the smooth backfitting estimator, the marginal integration estimator, and two versions...

Kernel density estimation in domains with boundaries is known to suffer from undesirable boundary effects. We show that in the case of smooth densities, a general and elegant approach is to estimate an extension of the density. The resulting estimators in domains with boundaries have biases and...

Nonparametric prediction of a random variable Y conditional on the value of an explanatory variable X is a classical and important problem in Statistics. The problem is significantly complicated if there are heterogeneously distributed measurement errors on the observed values of X used in...

We define a new bandwidth-dependent kernel density estimator that improves existing convergence rates for the bias, and preserves that of the variation, when the error is measured in L1. No additional assumptions are imposed to the extant literature.

Estimators for derivatives associated with a density function can be useful in identifying its modes and inflection points. In addition, these estimators play an important role in plug-in methods associated with bandwidth selection in nonparametric kernel density estimation. In this paper we...