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 both frequentist and 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. Also, the model can account for heteroscedastic measurement errors, if the information about measurement error standard deviations is known. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces posterior samples of the model parameters via Metropolis-Hastings within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo. 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.
- Version2.0.2
- R versionunknown
- LicenseGPL-2
- Needs compilation?No
- Last release03/21/2020
Documentation
Team
Hyungsuk Tak
Kisung You
Henghsiu Tsai
Insights
Last 30 days
This package has been downloaded 222 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.
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Last 365 days
This package has been downloaded 3,393 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 21, 2024 with 140 downloads.
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Dependencies
- Imports5 packages