Broadband log-periodogram regression of time series with long-range dependence

Log-periodogram Regression of Time Series with Long Range Dependence

This paper discusses the use of fractional exponential models (Robinson (1990), Beran (1994)) to model the spectral density f(x) of a covariance stationary process when f(x) may be decomposed as f(x) = x ?2d f (x), where f (x) is bounded and bounded away from zero. A form of log-periodogram regression technique is presented both in the parametric context (i.e. f (x) is a nite order exponential ...

Log-periodogram regression in asymmetric long memory

Filtered Log-periodogram Regression of long memory processes

Filtered log-periodogram regression estimation of the fractional differencing parameter d is considered. Asymptotic properties are derived and the effect of filtering on d̂ is investigated. It is shown that the estimator by Geweke and Porter-Hudak (1983) can be improved significantly using a simple family of filters. The essential improvement is based on a binary decision that is asymptotically ...

Bootstrapping the log-periodogram regression

Semiparametric estimation of the memory parameter in economic time series raises the problem of the small sample size and the poor approximation of the asymptotic distribution to the finite sample counterpart. This paper considers the bootstrap to improve the finite sample distribution of the popular log peridogram regression and shows that it can significantly reduce the error in the coverage ...

Specification Testing in Nonlinear Time Series with Long – Range Dependence

This paper proposes a model specification testing procedure for parametric specification of the conditional mean function in a nonlinear time series model with long–range dependence. An asymptotically normal test is established even when long–range dependence is involved. In order to implement the proposed test in practice using a simulated example, a bootstrap simulation procedure is establish...