carfima: Continuous-Time Fractionally Integrated ARMA Process for Irregularly Spaced Long-Memory Time Series Data

We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via frequentist or Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005)<doi:10.1111/j.1467-9868.2005.00522.x> and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces their posterior distributions via Metropolis within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo for posterior sampling. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.

Version: 1.0.0
Depends: R (≥ 2.2.0)
Imports: MASS (≥ 7.3-47), DEoptim (≥ 2.2-4), numDeriv (≥ 2016.8-1), truncnorm (≥ 1.0-7), invgamma (≥ 1.1)
Published: 2017-10-23
Author: Hyungsuk Tak and Henghsiu Tsai
Maintainer: Hyungsuk Tak <hyungsuk.tak at>
License: GPL-2
NeedsCompilation: no
In views: TimeSeries
CRAN checks: carfima results


Reference manual: carfima.pdf
Package source: carfima_1.0.0.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
OS X binaries: r-prerel: carfima_1.0.0.tgz, r-release: carfima_1.0.0.tgz


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