carfima (2.0.1)

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) 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.

Maintainer: Kisung You
Author(s): Hyungsuk Tak [aut] (<>), Henghsiu Tsai [aut], Kisung You [aut, cre] (<>)

License: GPL-2

Uses: cubature, DEoptim, invgamma, MASS, numDeriv, Rcpp, Rdpack, truncnorm

Released 11 months ago.