NTS (version 1.1.3)

mTAR.est: 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
)

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.

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.

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)