estimation method to estimate the single-index model without under-smoothing. Under some conditions, our estimator of the single … estimator and use them to define an optimal bandwidth for the purposes of index estimation. As a result we obtain a practically …

estimation method to estimate the single-index model without under-smoothing. Under some conditions, our estimator of the single … estimator and use them to define an optimal bandwidth for the purposes of index estimation. As a result we obtain a practically …

-neutral density estimator and the subjective density estimator. The former density can be represented as the second derivative w …

nonparametrically by using the ratio of the risk-neutral density estimator and the subjective density estimator. The risk …-neutral density is approximated by a weighted kernel density estimator with varying unknown weights for different observations, and … the subjective density is approximated by a kernel density estimator with equal weights. We represent the European call …

binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions. Our results are for …

-step approaches under a consistency approximation for the long run variance estimator. Based on these results we introduce modified PP …. -- nonparametric kernel estimator ; long run variance ; detrending ; one-step ; two-step …

-step approaches under a consistency approximation for the long run variance estimator. Based on these results we introduce modified PP …

penalized sieve minimum distance (SMD) estimator of the unknown functions that are identified via the conditional moment models … important applications: root-n asymptotic normality of the plug-in penalized SMD estimator of a weighted average derivative of a …