tsqn: GPH_estimate – R documentation

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GPH_estimate

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)

tsqn

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

v1.0.0

GPL (>= 2)

Authors

Higor Cotta, Valderio Reisen, Pascal Bondon and Céline Lévy-Leduc

Initial release

2017-03-20