Generates covariates describing certain types of intervention effects according to the definition by Fokianos and Fried (2010).
interv_covariate(n, tau, delta)integer value giving the number of observations the covariates should have.
integer vector giving the times where intervention effects occur.
numeric vector with constants specifying the type of intervention (see Details). Must be of the same length as tau.
A matrix with n rows and length(tau) columns. The generated covariates describing the interventions are the columns of the matrix.
The intervention effect occuring at time \(\tau\) is described by the covariate $$X_t = \delta^{t-\tau} I_{[\tau,\infty)}(t),$$ where \(I_{[\tau,\infty)}(t)\) is the indicator function which is 0 for \(t < \tau\) and 1 for \(t \geq \tau\). The constant \(\delta\) with \(0 \leq \delta \leq 1\) specifies the type of intervention. For \(\delta = 0\) the intervention has an effect only at the time of its occurence, for \(0 < \delta < 1\) the effect decays exponentially and for \(\delta = 1\) there is a persistent effect of the intervention after its occurence.
If tau and delta are vectors, one covariate is generated with tau[1] as \(\tau\) and delta[1] as \(\delta\), another covariate for the second elements and so on.
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.
Liboschik, T. (2016) Modelling count time series following generalized linear models. PhD Thesis TU Dortmund University, http://dx.doi.org/10.17877/DE290R-17191.
Liboschik, T., Kerschke, P., Fokianos, K. and Fried, R. (2016) Modelling interventions in INGARCH processes. International Journal of Computer Mathematics 93(4), 640--657, http://dx.doi.org/10.1080/00207160.2014.949250.
tsglm for fitting a GLM for time series of counts.
interv_test, interv_detect and interv_multiple for tests and detection procedures for intervention effects.
# NOT RUN {
interv_covariate(n=140, tau=c(84,100), delta=c(1,0))
# }
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