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....

We consider semi parametric estimation of the long-memory parameter of a stationaryprocess in the presence of an additive nonparametric mean function. We use a semi parametric Whittle type estimator, applied to the tapered, differenced series. Since the mean function is not necessarily...