We establish the validity of an Edgeworth expansion to the distribution of the maximum likelihood estimator of the parameter of a stationary, Gaussian, strongly dependent process. The result covers many types of ARFIMA models.

The paper proposes an original class of conditionally heteroskedastic models aimed to capture contemporaneous asymmetry. Not only past up and down moves of stock market returns have different impacts on the conditional variance, but also, positive and negative changes are governed by different...

In this paper the authors study the problem of non parametric estimation of an unknown regression function from dependent data with sub-Gaussian errors. As a particular case, they handle the autoregressive framework.

Median, a well known measure of central tendency, of a sample data x =(x1, x2, ... ,xn) is obtained through the traditional method as (xn-m + xm+1)/2, where m= int(n/2) and values of x are arranged in ascending (descending) order. Since, for odd n, n-m=m+1, the formula uses only one value from...

The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the...

As I document using evidence from a journal data repository that I manage, the datasets used in empirical work are getting larger. When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In addition, we need to worry about computational issues....