# uTAR.est: General Estimation of TAR Models In NTS: Nonlinear Time Series Analysis

## Description

General estimation of TAR models with known threshold values. It perform LS estimation of a univariate TAR model, and can handle multiple regimes.

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```uTAR.est( y, arorder = c(1, 1), thr = c(0), d = 1, thrV = NULL, include.mean = c(TRUE, TRUE), output = TRUE ) ```

## Arguments

 `y` time series. `arorder` AR order of each regime. The number of regime is the length of arorder. `thr` given threshold(s). There are k-1 threshold for a k-regime model. `d` delay for threshold variable, default is 1. `thrV` external threshold variable if any. If it is not NULL, thrV must have the same length as that of y. `include.mean` a logical value indicating whether constant terms are included. Default is TRUE. `output` a logical value for output. Default is TRUE.

## Value

uTAR.est returns a list with components:

 `data` the data matrix, y. `k` the number of regimes. `arorder` AR orders of regimes 1 and 2. `coefs` a k-by-(p+1) matrices, where `k` is the number of regimes. The i-th row shows the estimation results in regime i. `sigma` estimated innovational covariances for all the regimes. `thr` threshold value. `residuals` estimated innovations. `sresi` standardized residuals. `nobs` numbers of observations in different regimes. `delay` delay for threshold variable. `cnst` logical values indicating whether the constant terms are included in different regimes. `AIC` AIC value.

## Examples

 ```1 2 3 4``` ```phi=t(matrix(c(-0.3, 0.5,0.6,-0.3),2,2)) y=uTAR.sim(nob=200, arorder=c(2,2), phi=phi, d=2, thr=0.2, cnst=c(1,-1),sigma=c(1, 1)) thr.est=uTAR(y=y\$series, p1=2, p2=2, d=2, thrQ=c(0,1),Trim=c(0.1,0.9), method="RLS") est=uTAR.est(y=y\$series, arorder=c(2,2), thr=thr.est\$thr, d=2) ```

NTS documentation built on Aug. 6, 2020, 5:08 p.m.