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. …

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing … unknown, nonparametric, form, where series nonparametric estimates of the score function are employed in adaptive estimates of …

We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression … estimators are proved to be asymptotically normal, with the same asymptotic variance. They achieve the semiparametric efficiency … responses are imputed using the semiparametric regression method the empirical log-likelihood is asymptotically a scaled chi …

We develop in this paper a generalization of the Indirect Inference (II) to semi-parametric settings and termed Semi-parametric … examples based on semi-parametric stochastic volatility models. …