Statistical analysis of autoregressive fractionally integrated moving average models in R

Javier E. Contreras-Reyes; Wilfredo Palma

doi:10.1007/s00180-013-0408-7

Statistical analysis of autoregressive fractionally integrated moving average models in R

Javier E. Contreras-Reyes, Wilfredo Palma

Research output: Contribution to journal › Article › peer-review

45 Scopus citations

Abstract

The autoregressive fractionally integrated moving average (ARFIMA) processes are one of the best-known classes of long-memory models. In the package afmtools for R, we have implemented a number of statistical tools for analyzing ARFIMA models. In particular, this package contains functions for parameter estimation, exact autocovariance calculation, predictive ability testing and impulse response function computation, among others. Furthermore, the implemented methods are illustrated with applications to real-life time series.

Original language	English
Pages (from-to)	2309-2331
Number of pages	23
Journal	Computational Statistics
Volume	28
Issue number	5
DOIs	https://doi.org/10.1007/s00180-013-0408-7
State	Published - Oct 2013
Externally published	Yes

Keywords

ARFIMA models
Exact variance matrix
Forecasting
Impulse response functions
Long-memory time series
R
Whittle estimation

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10.1007/s00180-013-0408-7

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Statistical analysis of autoregressive fractionally integrated moving average models in R

Abstract

Keywords

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