Description Usage Arguments Value References Examples
Search for the threshold of a SETAR model for a given range of candidates for threshold values, and perform recursive LS estimation. The program uses a grid to search for threshold value. It is a conservative approach, but might be more reliable than the Li and Tong (2016) procedure.
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y |
a vector of time series. |
p1, p2 |
AR-orders of regime 1 and regime 2. |
d |
delay for threshold variable, default is 1. |
thrV |
threshold variable. if it is not null, thrV must have the same length as that of y. |
thrQ |
lower and upper limits for the possible threshold values. |
Trim |
lower and upper trimming to control the sample size in each regime. |
include.mean |
a logical value for including constant term. |
uTAR.grid returns a list with components:
data |
the data matrix, y. |
arorder |
AR orders of regimes 1 and 2. |
residuals |
estimated innovations. |
coefs |
a 2-by-(p+1) matrices. The first row show the estimation results in regime 1, and the second row shows these in regime 2. |
sigma |
estimated innovational covariance matrices of regimes 1 and 2. |
nobs |
numbers of observations in regimes 1 and 2. |
delay |
the delay for threshold variable. |
model1,model2 |
estimated models of regimes 1 and 2. |
cnst |
logical values indicating whether the constant terms are included in regimes 1 and 2. |
thr |
threshold value. |
D |
a set of possible threshold values. |
RSS |
residual sum of squares. |
information |
information criterion. |
sresi |
standardized residuals. |
Li, D., and Tong. H. (2016) Nested sub-sample search algorithm for estimation of threshold models. Statisitca Sinica, 1543-1554.
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