Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Modelling interventions in INGARCH processes. / Liboschik, T.; Kerschke, P.; Fokianos, K. et al.
In: International Journal of Computer Mathematics, Vol. 93, No. 4, 2016, p. 640-657.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Modelling interventions in INGARCH processes
AU - Liboschik, T.
AU - Kerschke, P.
AU - Fokianos, K.
AU - Fried, R.
PY - 2016
Y1 - 2016
N2 - We study different approaches for modelling intervention effects in time series of counts, focusing on the so-called integer-valued GARCH models. A previous study treated a model where an intervention affects the non-observable underlying mean process at the time point of its occurrence and additionally the whole process thereafter via its dynamics. As an alternative, we consider a model where an intervention directly affects the observation at its occurrence, but not the underlying mean, and then also enters the dynamics of the process. While the former definition describes an internal change of the system, the latter can be understood as an external effect on the observations due to e.g. immigration. For our alternative model we develop conditional likelihood estimation and, based on this, tests and detection procedures for intervention effects. Both models are compared analytically and using simulated and real data examples. We study the effect of model misspecification and computational issues.
AB - We study different approaches for modelling intervention effects in time series of counts, focusing on the so-called integer-valued GARCH models. A previous study treated a model where an intervention affects the non-observable underlying mean process at the time point of its occurrence and additionally the whole process thereafter via its dynamics. As an alternative, we consider a model where an intervention directly affects the observation at its occurrence, but not the underlying mean, and then also enters the dynamics of the process. While the former definition describes an internal change of the system, the latter can be understood as an external effect on the observations due to e.g. immigration. For our alternative model we develop conditional likelihood estimation and, based on this, tests and detection procedures for intervention effects. Both models are compared analytically and using simulated and real data examples. We study the effect of model misspecification and computational issues.
KW - change-point detection
KW - generalized linear models
KW - level shifts
KW - outliers
KW - time series of counts
U2 - 10.1080/00207160.2014.949250
DO - 10.1080/00207160.2014.949250
M3 - Journal article
VL - 93
SP - 640
EP - 657
JO - International Journal of Computer Mathematics
JF - International Journal of Computer Mathematics
IS - 4
ER -