Smooth nonparametric kernel density and regression estimators are studied when the data is strongly dependent. In particular, we derive Central (and Noncentral) Limit Theorems for the kernel density estimator of a multivariate Gaussian process and infinite-order moving average of an independent...

This paper provides limit theorems for special density matrix estimators and functionals of it for a bivariate co variance stationary process whose spectral density matrix has singularities not only at the origin but possibly at some other frequencies, and thus applies to time series exhibiting...

This paper is concerned with various issues related to inference in large dynamic panel data models (where both n and T increase without bound) in the presence of, possibly, strong cross-sectional dependence. Our first aim is to provide a Central Limit Theorem for estimators of the slope...