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:	2.0.1
Imports:	Rcpp, DEoptim, Rdpack, MASS, cubature, numDeriv, stats, utils, truncnorm, invgamma
LinkingTo:	Rcpp, RcppArmadillo, cubature
Published:	2019-01-23
Author:	Hyungsuk Tak [aut], Henghsiu Tsai [aut], Kisung You [aut, cre]
Maintainer:	Kisung You <kyou at nd.edu>
License:	GPL-2
NeedsCompilation:	yes
Materials:	README NEWS
In views:	TimeSeries
CRAN checks:	carfima results

Downloads:

Linking:

Please use the canonical form https://CRAN.R-project.org/package=carfima to link to this page.