Statistical analysis of autoregressive fractionally integrated moving average models in R

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 param...

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Bibliographic Details
Published in:Computational statistics 2013-10, Vol.28 (5), p.2309-2331
Main Authors: Contreras-Reyes, Javier E., Palma, Wilfredo
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Computation
Computational mathematics
Economic Theory/Quantitative Economics/Mathematical Methods
Impulse response
Mathematical analysis
Mathematical models
Mathematics and Statistics
Noise
Original Paper
Packages
Parameter estimation
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Statistical analysis
Statistics
Studies
Time series
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Summary: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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-013-0408-7