Invitation to Review for the Journal of Statistical Software

[Brandon Whitcher]

Dear Rmetrics Core Team,

Manuscript JSS784 "Estimation, Diagnostic and Forecasting Tools for ARFIMA
Models: The *afmtools* Package" has been submitted to the *Journal of
Statistical Software*.  Would someone from your team be willing to review
the manuscript and accompanying software?  I am aware that the package *
fArma* was written by members of your team.  The *afmtools* software is
written as an R package already available from
CRAN<http://cran.r-project.org/>or one of its mirror sites.

The title and abstract are attached to this email, along with the names of
the authors and keywords.  I have also attached a draft version of the
reviewer guidelines.  Please let me know within seven days if you will be
able to review the manuscript and software by email.

If you are unable to review this manuscript, would you please recommend one
or two other potential referees with expertise in this area?

If you do choose to review this manuscript, I will provide you with all
relevant files (essentially the manuscript and R code).  I would ask you to
complete your review within eight weeks.


Sincerely,

Brandon Whitcher PhD CStat
Associate Editor, Journal of Statistical Software
b.whitcher at imperial.ac.uk


Estimation, Diagnostic and Forecasting Tools for ARFIMA Models: The afmtools
Package

Javier Contreras and Wilfredo Palma

Abstract

In practice, several time series exhibit long-range dependence or
persistence in their
observations. This circumstance has motivated the development of a number of
estimation
and prediction methodologies to account for the slowly decaying
autocorrelations. One
of the most well known classes of long-memory models is the autoregressive
fractionally
integrated moving average (ARFIMA) process. In the package afmtools for R,
we have
implemented some of these statistical tools for analyzing ARFIMA models. In
particular,
this R package contains functions for parameter estimation, exact
autocovariance calcu-
lation, multi-step ahead forecasting, predictive ability testing, among
others. Finally, the
implemented methods are illustrated with applications to real-life time
series.

Keywords: ARFIMA models, long-memory time series, estimation, prediction, R
package.
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