The paper is concerned with the estimation of the long memory parameter in a conditionally heteroskedastic model proposed by Giraitis, Robinson and Surgailis (1999). We consider methods based on the partial sums of the squared observations which are similar in spirit to the classicla R/S...

In this paper we develop a general strategy for studying the effect on unbiased, nearest-neighbor walks of opening up or blocking a trap or neural site on a d-dimensional lattice.

This paper employs response surface regressions based on simulation experiments to calculate asymptotic distribution functions of the tests for cointegration proposed by Johansen. The paper provides accurate tables of asymptotic critical values. A program which can be used to calculate both...

This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analytical knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We...

We show that the empirical process of the squared residuals of an ARCH(p) sequence converges in distribution to a Gaussian process B (F(t)) + tf(t)E , where F is the distribution function of the squared innovations, f its derivative, {B(t), 0= t = 1} a Brownian bridge and E a normal random...

The usual formulation of probit models includes, as endogenous variables, both continuous latent variables and binary observable variables. The solution of simultaneous probit models involving only latent endogenous variables among the explanatory variables is straightforward, provided the...