Pooled Log Periodogram Regression

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 ...

Log-periodogram regression in asymmetric long memory

Unit Root Log Periodogram Regression By

Log periodogram (LP) regression is shown to be consistent and to have a mixed normal limit distribution when the memory parameter d 1⁄4 1. Gaussian errors are not required. The proof relies on a new result showing that asymptotically infinite collections of discrete Fourier transforms (dft’s) of a short memory process at the fundamental frequencies in the vicinity of the origin can be treated a...

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 ...

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 ...