This paper studies the asymptotic and nite-sample performance ofpenalized regression methods when different selectors of theregularization parameter are used under the assumption that the truemodel is, or is not, included among the candidate model. In the lattersetting, we relax assumptions in...

We establish sufficient conditions on durations that arestationary with finite variance and memory parameter $d \in[0,1/2)$ to ensure that the corresponding counting process $N(t)$satisfies $Var N(t) \sim C t^{2d+1}$ ($Cgt;0$) as $t\rightarrow \infty$, with the same memory parameter $d...

In this paper, we discuss two distinct multivariate time series models that extend the univariate ARFIMA model. We describe algorithms for computing the covariances of each model, for computing the quadratic form and approximating the determinant for maximum likelihood estimation, and for...

We propose a direct and convenient reduced-bias estimator of predictive regression coefficients, assuming that the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable. For the single-regressormodel, Stambaugh (1999)...

For long-memory time series, we show that the Toeplitz system sect;n(f)x = b can be solved inO(n log5=2 n) operations using a well-known version of the preconditioned conjugate gradient method, where sect;n(f) is the npound;n covariance matrix, f is the spectral density and b is a known vector....

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...