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

The skeleton of a TAR model is obtained by suppressing the noise term from the TAR model.

1 2 | ```
tar.skeleton(object, Phi1, Phi2, thd, d, p, ntransient = 500, n = 500,
xstart, plot = TRUE,n.skeleton = 50)
``` |

`object` |
a TAR model fitted by the tar function; if it is supplied, the model parameters and initial values are extracted from it |

`ntransient` |
the burn-in size |

`n` |
sample size of the skeleton trajectory |

`Phi1` |
the coefficient vector of the lower-regime model |

`Phi2` |
the coefficient vector of the upper-regime model |

`thd` |
threshold |

`d` |
delay |

`p` |
maximum autoregressive order |

`xstart` |
initial values for the iteration of the skeleton |

`plot` |
if True, the time series plot of the skeleton is drawn |

`n.skeleton` |
number of last n.skeleton points of the skeleton to be plotted |

The two-regime Threshold Autoregressive (TAR) model is given by the following formula:

*
Y_t = φ_{1,0}+φ_{1,1} Y_{t-1} +…+ φ_{1,p} Y_{t-p_1} +σ_1 e_t,
\mbox{ if } Y_{t-d}≤ r *

* Y_t = φ_{2,0}+φ_{2,1} Y_{t-1} +…+φ_{2,p_2} Y_{t-p}+σ_2 e_t,
\mbox{ if } Y_{t-d} > r.*

where r is the threshold and d the delay.

A vector that contains the trajectory of the skeleton, with the burn-in discarded.

Kung-Sik Chan

Tong, H. (1990) "Non-linear Time Series, a Dynamical System Approach," Clarendon Press Oxford. "Time Series Analysis, with Applications in R" by J.D. Cryer and K.S. Chan

1 2 3 |

TSA documentation built on July 2, 2018, 1:04 a.m.

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