mTAR.est | R Documentation |

Estimation of multivariate TAR models with given thresholds. It can handle multiple regimes.

```
mTAR.est(
y,
arorder = c(1, 1),
thr = c(0),
delay = c(1, 1),
thrV = NULL,
include.mean = c(TRUE, TRUE),
output = TRUE
)
```

`y` |
vector time series. |

`arorder` |
AR order of each regime. The number of regime is length of arorder. |

`thr` |
threshold value(s). There are k-1 threshold for a k-regime model. |

`delay` |
two elements (i,d) with "i" being the component and "d" the delay for threshold variable. |

`thrV` |
external threshold variable if any. If thrV is not null, it must have the same number of observations as y-series. |

`include.mean` |
logical values indicating whether constant terms are included. Default is TRUE for all. |

`output` |
a logical value indicating four output. Default is TRUE. |

mTAR.est returns a list with the following components:

`data` |
the data matrix, |

`k` |
the dimension of |

`arorder` |
AR orders of regimes 1 and 2. |

`beta` |
a ( |

`sigma` |
estimated innovational covariance matrices of regimes 1 and 2. |

`thr` |
threshold value. |

`residuals` |
estimated innovations. |

`sresi` |
standardized residuals. |

`nobs` |
numbers of observations in different regimes. |

`cnst` |
logical values indicating whether the constant terms are included in different regimes. |

`AIC` |
AIC value. |

`delay` |
two elements ( |

`thrV` |
values of threshold variable. |

```
phi1=matrix(c(0.5,0.7,0.3,0.2),2,2)
phi2=matrix(c(0.4,0.6,0.5,-0.5),2,2)
sigma1=matrix(c(1,0,0,1),2,2)
sigma2=matrix(c(1,0,0,1),2,2)
c1=c(0,0)
c2=c(0,0)
delay=c(1,1)
y=mTAR.sim(100,0,phi1,phi2,sigma1,sigma2,c1,c2,delay,ini=500)
est=mTAR.est(y$series,c(1,1),0,delay)
```

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