EXPAR
Fitting of Exponential Autoregressive (EXPAR) Model
The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) doi:10.2307/2335819 has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided.
- Version0.1.0
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release05/10/2024
Documentation
Team
Saikath Das
Achal Lama
Show author detailsRolesAuthorBishal Gurung
Show author detailsRolesAuthorKN Singh
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 530 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.
Last 365 days
This package has been downloaded 4,695 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 Sep 11, 2024 with 55 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports1 package