Filtered Log-Periodogram Regression of Long Memory Processes

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 in asymmetric long memory

On the Log Periodogram Regression Estimator of the Memory Parameter in Long Memory Stochastic Volatility Models

We consider semiparametric estimation of the memory parameter in a long memory stochastic volatility model+ We study the estimator based on a log periodogram regression as originally proposed by Geweke and Porter-Hudak ~1983, Journal of Time Series Analysis 4, 221–238!+ Expressions for the asymptotic bias and variance of the estimator are obtained, and the asymptotic distribution is shown to be...

Nonlinear Log-periodogram Regression for Perturbed Fractional Processes

This paper studies fractional processes that may be perturbed by weakly dependent time series. The model for a perturbed fractional process has a components framework in which there may be components of both long and short memory. All commonly used estimates of the long memory parameter (such as log periodogram (LP) regression) may be used in a components model where the data are affected by we...

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 ...