mTAR.est: Estimation of Multivariate TAR Models

In NTS: Nonlinear Time Series Analysis

Estimation of Multivariate TAR Models

Description

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

Usage

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

Arguments

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.

Value

mTAR.est returns a list with the following components:

data	the data matrix, y.
k	the dimension of y.
arorder	AR orders of regimes 1 and 2.
beta	a (p*k+1)-by-(2k) matrices. The first k columns show the estimation results in regime 1, and the second k columns show these in regime 2.
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 (i,d) with "i" being the component and "d" the delay for threshold variable.
thrV	values of threshold variable.

Examples

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)

NTS documentation built on Sept. 25, 2023, 1:08 a.m.