TY  - JOUR
AB  - A new software package for the Julia language, CountTimeSeries.jl, is under review, which provides likelihood based methods for integer-valued time series. The package’s functionalities are showcased in a simulation study on finite sample properties of Maximum Likelihood (ML) estimation and three real-life data applications. First, the number of newly infected COVID-19 patients is predicted. Then, previous findings on the need for overdispersion and zero inflation are reviewed in an application on animal submissions in New Zealand. Further, information criteria are used for model selection to investigate patterns in corporate insolvencies in Rhineland-Palatinate. Theoretical background and implementation details are described, and complete code for all applications is provided online. The CountTimeSeries package is available at the general Julia package registry.
AU  - Stapper, Manuel
DA  - 2021-05-25
DO  - 10.3390/e23060666
KW  - count data
KW  - time series analysis
KW  - Julia programming language
LA  - eng
N1  - Entropy 23 (2021) 6, 666, 1-22
N1  - Supplementary Materials are available online at https://www.mdpi.com/article/10.3390/e23060666/s1, Julia and R code for all four applications.
PY  - 2021-05-25
TI  - Count Data Time Series Modelling in Julia—The CountTimeSeries.jl Package and Applications
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:6-37039752424
Y2  - 2024-11-22T01:14:03
ER  -