Estimation of a MTAR model for some data
Compute by Bayesian methodology a MTAR model for some data
auto_mtar(Yt, Zt = NULL, Xt = NULL, l0_min = 2, l0_max = 3, maxorders = list(pj = 2,qj = 0,dj = 0), niter = 3000, chain = FALSE, method = 'KUO',parallel = FALSE)
Yt |
matrix type object, observed process. Not NULL |
Zt |
matrix type object, threshold process. Default NULL |
Xt |
matrix type object, covariate process. Default NULL |
l0_min |
numeric type between 1 and 4, number of regimes minimum to consider. Default 2 |
l0_max |
numeric type between 1 and 4, number of regimes maximum to consider. Default 3 |
maxorders |
list type object with names (pj,qj,dj), maximum lags consider for the processes in each regime. Default pj = 2, qj = 0,dj = 0 |
niter |
numeric type, number of runs for every estimation. Default 3000 |
chain |
logical type, if return chains of estimations parameters and values (if missing) |
method |
character type, must be one “KUO” or “SSVS” |
parallel |
logical, if |
The default arguments are designed for rapid estimation of models for any data (Yt, Zt and Xt). Returns the fit of MTAR model.
The function conducts Bayesian estimation with “niter” chains of the number of regimes with maximum “l0” and within the maximum lags orders provided “maxorders”.
It can be a little be faster when used “parallel” for parallel package.
Return list type object
tsregime |
class “ |
numreg |
class “ |
pars |
class “ |
Valeria Bejarano vbejaranos@unal.edu.co, Sergio Calderon sacalderonv@unal.edu.co & Andrey Rincon adrincont@unal.edu.co
Calderon, S. and Nieto, F. (2017) Bayesian analysis of multivariate threshold autoregress models with missing data. Communications in Statistics - Theory and Methods 46 (1):296–318. doi:10.1080/03610926.2014.990758.
data('datasim')
data = datasim$Sim
auto = auto_mtar(Yt = data$Yt, Zt = data$Zt,niter = 1000)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.