ARRLS.sim: Simulation of Autoregressive Random Level Shift processes.

In FunWithR/LongMemoryTS: Long Memory Time Series

Description

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

Usage

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

Arguments

T	length of the desired series.
phi	autoregressive parameter that determines the persistence of the shifts. For phi=1 the process is a "stationary RLS" and for phi=0 the process is a non stationary RLS.
sig.shifts	standard deviation of the shifts.
prob	shift probability. For rare shifts p*/T, where p* is the expected number of shifts in the sample.
sig.noise	standard deviation of the noise component. Default is sig.noise=0.
const	mean of the process. Default is const=0.
trend	trend of the process. Default is trend=0.
burnin	length of the burnin period used. Default is burnin=100.

Details

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Author(s)

Christian Leschinski

References

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.

Examples

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

FunWithR/LongMemoryTS documentation built on May 12, 2019, 10:29 p.m.