Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/interv_covariate.R

Generates covariates describing certain types of intervention effects according to the definition by Fokianos and Fried (2010).

1 | ```
interv_covariate(n, tau, delta)
``` |

`n` |
integer value giving the number of observations the covariates should have. |

`tau` |
integer vector giving the times where intervention effects occur. |

`delta` |
numeric vector with constants specifying the type of intervention (see Details). Must be of the same length as |

The intervention effect occuring at time *τ* is described by the covariate

*X_t = δ^(t-τ) I(t>=τ),*

where *I(t>=τ)* is the indicator function which is 0 for *t < τ* and 1 for *t >= τ*. The constant *δ* with *0 <= δ <= 1* specifies the type of intervention. For *δ = 0* the intervention has an effect only at the time of its occurence, for *0 < δ < 1* the effect decays exponentially and for *δ = 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 *τ* and `delta[1]`

as *δ*, another covariate for the second elements and so on.

A matrix with `n`

rows and `length(tau)`

columns. The generated covariates describing the interventions are the columns of the matrix.

Tobias Liboschik

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.

1 | ```
interv_covariate(n=140, tau=c(84,100), delta=c(1,0))
``` |

```
interv_1 interv_2
[1,] 0 0
[2,] 0 0
[3,] 0 0
[4,] 0 0
[5,] 0 0
[6,] 0 0
[7,] 0 0
[8,] 0 0
[9,] 0 0
[10,] 0 0
[11,] 0 0
[12,] 0 0
[13,] 0 0
[14,] 0 0
[15,] 0 0
[16,] 0 0
[17,] 0 0
[18,] 0 0
[19,] 0 0
[20,] 0 0
[21,] 0 0
[22,] 0 0
[23,] 0 0
[24,] 0 0
[25,] 0 0
[26,] 0 0
[27,] 0 0
[28,] 0 0
[29,] 0 0
[30,] 0 0
[31,] 0 0
[32,] 0 0
[33,] 0 0
[34,] 0 0
[35,] 0 0
[36,] 0 0
[37,] 0 0
[38,] 0 0
[39,] 0 0
[40,] 0 0
[41,] 0 0
[42,] 0 0
[43,] 0 0
[44,] 0 0
[45,] 0 0
[46,] 0 0
[47,] 0 0
[48,] 0 0
[49,] 0 0
[50,] 0 0
[51,] 0 0
[52,] 0 0
[53,] 0 0
[54,] 0 0
[55,] 0 0
[56,] 0 0
[57,] 0 0
[58,] 0 0
[59,] 0 0
[60,] 0 0
[61,] 0 0
[62,] 0 0
[63,] 0 0
[64,] 0 0
[65,] 0 0
[66,] 0 0
[67,] 0 0
[68,] 0 0
[69,] 0 0
[70,] 0 0
[71,] 0 0
[72,] 0 0
[73,] 0 0
[74,] 0 0
[75,] 0 0
[76,] 0 0
[77,] 0 0
[78,] 0 0
[79,] 0 0
[80,] 0 0
[81,] 0 0
[82,] 0 0
[83,] 0 0
[84,] 1 0
[85,] 1 0
[86,] 1 0
[87,] 1 0
[88,] 1 0
[89,] 1 0
[90,] 1 0
[91,] 1 0
[92,] 1 0
[93,] 1 0
[94,] 1 0
[95,] 1 0
[96,] 1 0
[97,] 1 0
[98,] 1 0
[99,] 1 0
[100,] 1 1
[101,] 1 0
[102,] 1 0
[103,] 1 0
[104,] 1 0
[105,] 1 0
[106,] 1 0
[107,] 1 0
[108,] 1 0
[109,] 1 0
[110,] 1 0
[111,] 1 0
[112,] 1 0
[113,] 1 0
[114,] 1 0
[115,] 1 0
[116,] 1 0
[117,] 1 0
[118,] 1 0
[119,] 1 0
[120,] 1 0
[121,] 1 0
[122,] 1 0
[123,] 1 0
[124,] 1 0
[125,] 1 0
[126,] 1 0
[127,] 1 0
[128,] 1 0
[129,] 1 0
[130,] 1 0
[131,] 1 0
[132,] 1 0
[133,] 1 0
[134,] 1 0
[135,] 1 0
[136,] 1 0
[137,] 1 0
[138,] 1 0
[139,] 1 0
[140,] 1 0
```

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