In this paper, we consider a partially linear panel data model with cross-sectional dependence and non-stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then...

In this paper, we consider a random quadratic form of strictly stationary processes. A central limit theorem for the quadratic form is established and an application to nonparametric series regression is given.

Consider the model Yt = [beta]Yt-1 + g(Yt-2) + [var epsilon]t for t [greater-or-equal, slanted] 3. Here g is an unknown function, [beta] is an unknown parameter to be estimated and [var epsilon]t are i.i.d. random error with zero 0 and variance [sigma]2 and [var epsilon]t are independent of Ys...

This paper establishes a general moment inequality for spatial processes satisfying the [alpha]-mixing condition [cf., Tran, 1990. Kernel density estimation on random fields. J. Multivariate Analy. 34, 37-53]. Such a general moment inequality is a nontrivial extension of the corresponding result...

We propose a sound approach to bandwidth selection in nonparametric kernel testing. The main idea is to find an Edgeworth expansion of the asymptotic distribution of the test concerned. Due to the involvement of a kernel bandwidth in the leading term of the Edgeworth expansion, we are able to...

This paper considers a class of nonstationary Gaussian processes with possible long-range dependence (LRD) and intermittency. The author proposes a new estimation method to simultaneously estimate both the LRD and intermittency parameter. An application of the proposed estimation method to a...

Nonparametric methods have been very popular in the last couple of decades in time series and regression, but no such development has taken place for spatial models. A rather obvious reason for this is the curse of dimensionality. For spatial data on a grid evaluating the conditional mean given...

Nonparametric methods have been very popular in the last couple of decades in time series and regression, but no such development has taken place for spatial models. A rather obvious reason for this is the curse of dimensionality. For spatial data on a grid evaluating the conditional mean given...