We propose robust methods for inference on the effect of a treatment variable on a scalar outcome in the presence of very many controls. Our setting is a partially linear model with possibly non-Gaussian and heteroscedastic disturbances where the number of controls may be much larger than the...

This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric...

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of...

This paper is concerned with estimating the mean of a random variable Y conditional on a vector of covariates X under weak assumptions about the form of the conditional mean function. Fully nonparametric estimation is usually unattractive when X is multidimensional because estimation precision...

In the context of the partially linear semiparametric model examined by Robinson (1988), we show that root-n-consisten estimation results established using kernel and series methods can also be obtained by using k-nearest-neighbor (k-nn) method.

We consider the problem of estimating a partially linear panel data model whenthe error follows an one-way error components structure. We propose a feasiblesemiparametric generalized least squares (GLS) type estimator for estimating the coefficient of the linear component and show that it is...

In this paper, we consider the application of the empirical likelihood method to partially linear model. Unlike the usual cases, we first propose an approximation to the residual of the model to deal with the nonparametric part so that Owen's (1990) empirical likelihood approach can be applied....

Su and Jin (2010) develop for partially linear spatial autoregressive (PL-SAR) model a profile quasimaximum likelihood based estimation procedure. More recently, Su (2011) proposes for this model a semiparametric GMM estimator. However, both of them can be computationally challenging for applied...

Non-standard distributional approximations have received considerable attention in recent years. They often provide more accurate approximations in small samples, and theoretical improvements in some cases. This paper shows that the seemingly unrelated "?many instruments asymptotics" ?and...

This paper provides new estimates of a time-varying NAIRU for Germany taking account of the structural break caused by German unification using two alternative estimators, the Kalman-Filter and the partially linearmodel. Estimating a standard Phillips curve, the sumof coefficients associated...