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)...

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...

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 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,...