LS.lognorm {TAR} | R Documentation |
Estimate a log-normal TAR model using Least Square method given the structural parameters.
Description
This function estimate a log-normal TAR model using Least Square method given the structural parameters, i.e. the number of regimes, thresholds and autoregressive orders.
Usage
LS.lognorm(Z, X, l, r, K)
Arguments
Z |
The threshold series |
X |
The series of interest |
l |
The number of regimes. |
r |
The vector of thresholds for the series |
K |
The vector containing the autoregressive orders of the |
Details
The TAR model is given by
when for som
(
).
the
is the threshold process,
is the number of regimes,
is the autoregressive order in the regime
.
with
denote the autoregressive coefficients, while
denote the variance weights.
Value
The function returns the autoregressive coefficients matrix theta and variance weights H. Rows of the matrix theta represent regimes
Author(s)
Hanwen Zhang <hanwenzhang at usantotomas.edu.co>
References
Nieto, F. H. (2005), Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data. Communications in Statistics. Theory and Methods, 34; 905-930
See Also
Examples
Z<-arima.sim(n=500,list(ar=c(0.5)))
l <- 2
r <- 0
K <- c(2,1)
theta <- matrix(c(1,0.5,-0.3,-0.5,-0.7,NA),nrow=l)
H <- c(1, 1.3)
X <- simu.tar.lognorm(Z,l,r,K,theta,H)
ts.plot(X)
LS.lognorm(Z,X,l,r,K)