tscount:An R package for analysis of count time series following generalized linear models

Liboschik, T. and Fokianos, K. and Fried, R. (2017) tscount:An R package for analysis of count time series following generalized linear models. Journal of Statistical Software, 82 (5). ISSN 1548-7660

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Abstract

The R package tscount provides likelihood-based estimation methods for analysis and modeling of count time series following generalized linear models. This is a flexible class of models which can describe serial correlation in a parsimonious way. The conditional mean of the process is linked to its past values, to past observations and to potential covariate effects. The package allows for models with the identity and with the logarithmic link function. The conditional distribution can be Poisson or negative binomial. An important special case of this class is the so-called INGARCH model and its log-linear extension. The package includes methods for model fitting and assessment, prediction and intervention analysis. This paper summarizes the theoretical background of these methods. It gives details on the implementation of the package and provides simulation results for models which have not been studied theoretically before. The usage of the package is illustrated by two data examples. Additionally, we provide a review of R packages which can be used for count time series analysis. This includes a detailed comparison of tscount to those packages.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Statistical Software
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
127727
Deposited By:
Deposited On:
26 Sep 2018 10:04
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 06:44