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

Description Usage Arguments Details References Examples

View source: R/ARRLS_sim.R

Description

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

Usage

1
2
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. Usually 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

add details here

References

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.

Examples

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

FunWithR/LongMemoryTS documentation built on June 9, 2018, 12:22 a.m.