Description Usage Arguments Details References Examples

Simulation of a AR-RLS process as discussed in Leschinski and Kruse (2014) and Xu and Perron (2014).

1 2 | ```
ARRLS.sim(T, phi, sig.shifts, prob, sig.noise = 0, const = 0, trend = 0,
burnin = 100)
``` |

`T` |
length of the desired series. |

`phi` |
autoregressive parameter that determines the persistence of the shifts.
For |

`sig.shifts` |
standard deviation of the shifts. |

`prob` |
shift probability. Usually p*/T, where p* is the expected number of shifts in the sample. |

`sig.noise` |
standard deviation of the noise component. Default is |

`const` |
mean of the process. Default is |

`trend` |
trend of the process. Default is |

`burnin` |
length of the burnin period used. Default is |

add details here

Leschinski, C. H. and Kruse, R. (2014): Testing for unit roots in random level shift processes. Mimeo.

Xu, J. and Perron, P. (2014): Forecasting return volatility: Level shifts with varying jump probability and mean reversion. International Journal of Forecasting, 30, pp. 449-463.

1 | ```
ts.plot(ARRLS.sim(T=500,phi=0.5, sig.shift=1, prob=0.05), ylab=expression(X[t]))
``` |

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