nonparametric and semiparametric specifications. …

We propose a new estimator for nonparametric regression based on local likelihood estimation using an estimated error … score function obtained from the residuals of a preliminary nonparametric regression. We show that our estimator is …

is based on semiparametric efficient estimation procedures for a seemingly unrelated regression model where the … dimensionality problem that typically arises in multivariate semiparametric estimation procedures, because the multivariate …

In a number of semiparametric models, smoothing seems necessary in order to obtain estimates of the parametric … estimates of semiparametric index models. Approaches to bias-reduction are discussed. We also develop a higher order expansion … sample performance of the methods is investigated by means of Monte Carlo simulations from a Tobit model. …

We investigate a new separable nonparametric model for time series, which includes many ARCH models and AR models …

Nonparametric regression is developed for data with both a temporal and a cross-sectional dimension. The model includes … heteroscedasticity. A simple nonparametric estimate is shown to be dominated by a GLS-type one. Asymptotically optimal bandwidth choices …

Normal distribution with a faster rate of convergence than unrestricted nonparametric alternatives. Their small sample …