GPH_estimate: Classical and Robust Geweke and Porter-Hudak (GPH) estimators...

In tsqn: Applications of the Qn Estimator to Time Series (Univariate and Multivariate)

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)

Example output

Loading required package: robustbase
Loading required package: MASS
Loading required package: fracdiff
[1] 0.3943987
[1] 0.4133843
[1] 0.4130925

tsqn documentation built on May 2, 2019, 7:43 a.m.