data.gen.tar: Generate a two-regime threshold autoregressive (TAR) process.

Description Usage Arguments Details Value References Examples

View source: R/data_gen_TAR.R

Description

Generate a two-regime threshold autoregressive (TAR) process.

Usage

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data.gen.tar(
  nobs,
  ndim = 9,
  phi1 = c(0.6, -0.1),
  phi2 = c(-1.1, 0),
  theta = 0,
  d = 2,
  p = 2,
  noise = 0.1
)

Arguments

nobs

the data length to be generated

ndim

The number of potential predictors (default is 9)

phi1

the coefficient vector of the lower-regime model

phi2

the coefficient vector of the upper-regime model

theta

threshold

d

delay

p

maximum autoregressive order

noise

the white noise in the data

Details

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}+σ_1 e_t, \mbox{ if } Y_{t-d}≤ r

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

where r is the threshold and d the delay.

Value

A list of 2 elements: a vector of response (x), and a matrix of potential predictors (dp) with each column containing one potential predictor.

References

Cryer, J. D. and K.-S. Chan (2008). Time Series Analysis With Applications in R Second Edition Springer Science+ Business Media, LLC.

Examples

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# TAR2 model from paper with total 9 dimensions
data.tar<-data.gen.tar(500)
plot.ts(cbind(data.tar$x,data.tar$dp))

synthesis documentation built on May 3, 2021, 9:07 a.m.