tscount: An R package for analysis of count time series following generalized linear models
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Date
2015
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Abstract
The R package tscount provides likelihood-based estimation methods for analysis and
modelling of count time series following generalized linear models. This is a
exible 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 e ects. 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 tting and assessment, prediction and
intervention analysis. This paper summarizes the theoretical background of these methods
with references to the literature for further details. 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 demonstrated by two data examples.
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Table of contents
Keywords
intervention analysis, serial correlation, regression model, R, prediction, model selection, mixed Poisson