Analysis of Count Time Series

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Description

Collection of R functions for analysis of count time series. Currently the focus is on count time series following generalised linear models.

Details

See the main function tsglm for more details on the usage of the package. There is a vignette available which introduces the functionality of the package and its underlying statistical methods (vignette("tsglm", package="tscount")).

Author(s)

Tobias Liboschik <liboschik@statistik.tu-dortmund.de>

References

Christou, V. and Fokianos, K. (2014) Quasi-likelihood inference for negative binomial time series models. Journal of Time Series Analysis 35(1), 55–78, http://dx.doi.org/10.1002/jtsa.12050.

Christou, V. and Fokianos, K. (2015) Estimation and testing linearity for non-linear mixed poisson autoregressions. Electronic Journal of Statistics 9, 1357–1377, http://dx.doi.org/10.1214/15-EJS1044.

Ferland, R., Latour, A. and Oraichi, D. (2006) Integer-valued GARCH process. Journal of Time Series Analysis 27(6), 923–942, http://dx.doi.org/10.1111/j.1467-9892.2006.00496.x.

Fokianos, K. and Fried, R. (2010) Interventions in INGARCH processes. Journal of Time Series Analysis 31(3), 210–225, http://dx.doi.org/10.1111/j.1467-9892.2010.00657.x.

Fokianos, K., and Fried, R. (2012) Interventions in log-linear Poisson autoregression. Statistical Modelling 12(4), 299–322. http://dx.doi.org/10.1177/1471082X1201200401.

Fokianos, K., Rahbek, A. and Tjostheim, D. (2009) Poisson autoregression. Journal of the American Statistical Association 104(488), 1430–1439, http://dx.doi.org/10.1198/jasa.2009.tm08270.

Fokianos, K. and Tjostheim, D. (2011) Log-linear Poisson autoregression. Journal of Multivariate Analysis 102(3), 563–578, http://dx.doi.org/10.1016/j.jmva.2010.11.002.

Liboschik, T., Kerschke, P., Fokianos, K. and Fried, R. (2014) Modelling interventions in INGARCH processes. International Journal of Computer Mathematics (published online), http://dx.doi.org/10.1080/00207160.2014.949250.

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