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 -