This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. These data-driven models seek to replace the "classical" parametric models of the past, which were rigid and often linear. Chapters by...

We consider the local linear generalized method of moment (GMM) estimation of functional coefficient models with a mix of discrete and continuous data and in the presence of endogenous regressors. We establish the asymptotic normality of the estimator and derive the optimal instrumental variable...

Recently Martins-Filho and Yao (J Multivar Anal 100:309–333, <CitationRef CitationID="CR7">2009</CitationRef>) have proposed a two-step estimator of nonparametric regression function with parametric error covariance and demonstrate that it is more efficient than the usual LLE. In the present paper we demonstrate that MY’s estimator...</citationref>

In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R²) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being...

In this paper we propose a revised version of (bagging) <bold>b</bold>ootstrap <bold>aggr</bold>egat<bold>ing</bold> as a forecast combination method for the out-of-sample forecasts in time series models. The revised version explicitly takes into account the dependence in time series data and can be used to justify the validity of...

We propose a semiparametric conditional covariance (SCC) estimator that combines the first-stage parametric conditional covariance (PCC) estimator with the second-stage nonparametric correction estimator in a multiplicative way. We prove the asymptotic normality of our SCC estimator, propose a...

We propose a local linear functional coefficient estimator that admits a mix of discrete and contin- uous data for stationary time series. Under weak conditions our estimator is asymptotically normally distributed. A small set of simulation studies is carried out to illustrate the ï¬nite...

We propose a semiparametric conditional covariance (SCC) estimator that combines the ï¬rst-stage parametric conditional covariance (PCC) estimator with the second-stage nonparametric correction estimator in a multiplicative way. We prove the asymptotic normality of our SCC estimator, propose a...