Nonparametric regression techniques provide an e ective way of identifying and examiningstructure in regression data The standard approaches to nonparametric regression suchas local polynomial and smoothing spline estimators are sensitive to unusual observations and alternatives designed to be...

We consider a common components model for multivariate fractional cointegration, in which the s cedil; 1 components have different memory parameters. The cointegrating rank is allowed to exceed 1. The true cointegrating vectors can be decomposed into orthogonal fractional cointegrating subspaces...

We consider a fractional exponential, or FEXP estimator of the memory parameter of a stationary Gaussian long-memory time series. The estimator is constructed by fitting a FEXP model of slowly increasing dimension to the log periodogram at all Fourier frequencies by ordinary least squares, and...

We study the properties of MallowsAtilde;cent;Acirc; Acirc; CL criterion for selecting a fractional exponential (FEXP) model for a Gaussian long-memory time series. The aim is to minimize the mean squared error of a corresponding regression estimator dFEXP of the memory parameter, d. Under...

We propose a new complex-valued taper and derive the properties of a tapered Gaussian semiparametric estimator of the long-memory parameter d Atilde; Acirc; Atilde; Acirc; (-0.5, 1.5). The estimator and its accompanying theory can be applied to generalized unit root testing. In the proposed method,...

We consider semiparametric estimation of the memory parameter in a modelwhich includes as special cases both the long-memory stochasticvolatility (LMSV) and fractionally integrated exponential GARCH(FIEGARCH) models. Under our general model the logarithms of the squaredreturns can be decomposed...

We propose a new semiparametric estimator of the degree of persistence in volatility forlong memory stochastic volatility (LMSV) models. The estimator uses the periodogram ofthe log squared returns in a local Whittle criterion which explicitly accounts for the noise term in the LMSV model....