Difficulties with inference in predictive regressions are generally attributed tostrong persistence in the predictor series. We show that the major source of the problem is actually the nuisance intercept parameter and propose basing inference on the Restricted Likelihood,which is free of such...

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...

We study the small sample behaviour of two goodness-of-fit tests for time series models whichhave been proposed recently in the literature. Both tests are generalizations of the popular Box-Ljung-Pierce portmanteau test, one in the time domain and the other in the frequency domain.The tests are...

We present a goodness of fit test for time series models based on the discrete spectral averageestimator. Unlike current tests of goodness of fit, the asymptotic distribution of our test statisticallows the null hypothesis to be either a short or long range dependence model. Our test isin the...

We propose and derive the asymptotic distribution of a tapered narrow-band least squares estimator(NBLSE) of the cointegration parameter Atilde;ƒAcirc;ŽAtilde;‚Acirc;² in the framework of fractional cointegration.This tapered estimator is invariant to deterministic polynomial trends. In particular,...

We propose and derive the asymptotic distribution of a tapered narrow-band least squaresestimator (NBLSE) of the cointegration parameter Icirc;² in the framework of fractional cointegration. Thistapered estimator is invariant to deterministic polynomial trends. In particular, we allow for...

Difficulties with inference in predictive regressions are generally attributed to strong persistence in the predictor series. We show that the major source of the problem is actually the nuisance intercept parameter and propose basing inference on the Restricted Likelihood,which is free of such...