BMTAR (version 0.1.1)

auto_mtar: Estimation of a MTAR model for some data

Description

Compute by Bayesian methodology a MTAR model for some data

Usage

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)

Arguments

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 parallel package should be used. Default FALSE

Value

Return list type object

tsregime

class “tsregime” object, if missing values completed with estimations

numreg

class “regime_number”, number of regimes estimated

pars

class “regime_model” object with final estimations of parameters

Details

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.

References

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<U+2013>318. doi:10.1080/03610926.2014.990758.

Examples

# NOT RUN {
  data('datasim')
  data = datasim$Sim
  
# }
# NOT RUN {
auto = auto_mtar(Yt = data$Yt, Zt = data$Zt,niter = 1000)
# }