GPH_estimate {tsqn}

Classical and Robust Geweke and Porter-Hudak (GPH) estimators for the long-memory parameter d of a long-range dependent stationary processes

Description

Estimate the fractional (or “memory”) parameter d of long-range dependent stationary processes by the method of Geweke and Porter-Hudak (GPH). (GPH-M) and (GPH-Qn) correspond to the estimators devised by Reisen et al. (2017) and Molinares (2009), respectively.

Usage

GPH_estimate(series, bandw.exp = 0.7, method = "GPH")

Arguments

series

univariate time series

bandw.exp

the bandwidth used in the regression equation

method

character string giving the type of GPH to be computed. Allowed values are "GPH" (the default), "GPH-M" or "GPH-Qn".

Value

d GPH estimate

sd.as asymptotic standard deviation

sd.reg standard error deviation

Author(s)

Valderio Reisen, Céline Lévy-Leduc and Higor Cotta.

References

Reisen, V. A. and Lévy-Leduc, C. and Taqqu, M. (2017) An M-estimator for the long-memory parameter. To appear in Journal of Statistical Planning and Inference.

Molinares, F. F. and Reisen, V. A., and Cribari-Neto, F. (2009) Robust estimation in long-memory processes under additive outliers. Journal of Statistical Planning and Inference, 139, 2511–2525. #' @references Geweke, J. and Porter-Hudak, S. (1983) The estimation and application of long memory time series models. Journal of Time Series Analysis, 4, 221–238.

Examples

library(fracdiff)
simseries <- fracdiff.sim(1500, d = 0.3)
GPH_estimate(simseries$series,method="GPH")$d
## Not run: 
GPH_estimate(simseries$series,method="GPH-Qn")$d
GPH_estimate(simseries$series,method="GPH-M")$d

## End(Not run)