This paper gives a selective review on the recent developments of nonparametric and semiparametric panel data models. We focus on the conventional panel data models with one-way error component structure, partially linear panel data models, varying coe¢ cient panel data models, nonparametric panel data models with multi-factor error structure, and nonseparable nonparametric panel data models. F...

KEY FINDINGS Following a long period of fairly steady increase, the U.S. preterm birth rate declined for the second straight year in 2008 to 12.3 percent, from 12.8 percent in 2006. This marks the first 2-year decline in the preterm birth rate in nearly three decades. Preterm birth rates declined from 2006 to 2008 for mothers of all age groups under age 40, for the three largest race and Hispan...

In the estimation of a dose-response curve, parametric models are straightforward and efficient but subject to model misspecifications; nonparametric methods are robust but less efficient. As a compromise, we propose a semiparametric approach that combines the advantages of parametric and nonparametric curve estimates. In a mixture form, our estimator takes a weighted average of the parametric ...

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel es...

In this paper, we propose a new diagnostic test for residual cross–section independence in a nonparametric panel data model. The proposed nonparametric cross–section dependence (CD) test is a nonparametric counterpart of an existing parametric CD test proposed in Pesaren (2004) for the parametric case. We establish an asymptotic distribution of the proposed test statistic under the null hypothe...

We congratulate the authors for a stimulating paper (referred to as HL in the following). As the authors correctly stated, the number of variables does not affect the optimal rate of convergence in a regular parametric model, but it does affect the optimal rate of convergence in nonparametric models. To be more precise, the optimal rate of convergence in a nonparametric function estimation prob...

We consider the general problem of constructing nonparametric Bayesian models on infinite-dimensional random objects, such as functions, infinite graphs or infinite permutations. The problem has generated much interest in machine learning, where it is treated heuristically, but has not been studied in full generality in nonparametric Bayesian statistics, which tends to focus on models over prob...

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel es...

Systematic sampling is frequently used in natural resource and other surveys, because of its ease of implementation and its design efficiency. An important drawback of systematic sampling, however, is that no direct estimator of the design variance is available. We describe a new estimator of the model-based expectation of the design variance, under a nonparametric model for the population. The...

We derive a strong approximation of a local polynomial estimator (LPE) in nonparametric autoregression by an LPE in a corresponding nonparame-tric regression model. This generally suggests the application of regression-typical tools for statistical inference in nonparametric autoregressive models. It provides an important simpliication for the bootstrap method to be used: It is enough to mimic ...