BMTAR (version 0.1.1)

mtarnumreg: Estimation of the number of regimes in a MTAR model

Description

Compute estimation of number of regimes by NAIC or Carlin and Chib methodology for a MTAR model

Usage

mtarnumreg(ini_obj, level = 0.95, burn_m = NULL,niter_m = 1000,
iterprev = 500, chain_m = FALSE, list_m = FALSE,
NAIC = FALSE,ordersprev = list(maxpj = 2,maxqj = 0,maxdj = 0),
parallel = FALSE)

Arguments

ini_obj

class “regime_inipars” object, here specificate l0_min, l0_max and method. Not NULL. Default l0_min = 2, l0_max = 3, method = 'KUO'

level

numeric type, confident interval for estimations. Default 0.95

burn_m

numeric type, number of initial runs. Default NULL (10% of niter)

niter_m

numeric type, number of runs of MCMC. Default 1000

iterprev

numeric type, number of runs for pseudo values. Default 500

chain_m

logical type, if return chains of parameters. Default FALSE

list_m

logical type, if return list of regimes considered. Default FALSE

NAIC

logical type, if return estimation of number of regimes by NAIC (not run Carlin and Chip for l). Default FALSE

ordersprev

list type object with names (maxpj,maxqj,maxdj), maximum number of lags of each process consider in the pseudo values for each number of regimes considered . Default maxpj = 2,maxqj = 0, maxdj = 0

parallel

logical type, if package parallel should be used. Default FALSE

Value

Return a list type object of class “regime_number”

tsregime

ini_obj$tsregime_obj

list_m

if list_m TRUE list of models considered

m_chain

if chain_m TRUE chains of m

estimates

table of the proportions of m estimated

final_m

numeric type, final number of regimes estimated

If NAIC TRUE

tsregime

ini_obj$tsregime_obj

list_m

list of consider models

NAIC

list type of NAIC for each considered model

NAIC_final_m

numeric type, final number of regimes by this criteria

Details

Two proposals to identify or estimate the number of regimes l are implemented. Metropolised Carlin and Chib methodology takes into account the changing dimension in the parameter vector when the number of regimes changes, that proposal is Bayesian model selection. Other methodology consists in calculating the information criterion NAIC.

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
initial = mtarinipars(tsregime_obj = data$Sim,
list_model = list(l0_max = 3),method = 'KUO')
# }
# NOT RUN {
estim = mtarnumreg(ini_obj = initial,iterprev = 500,niter_m = 500,
burn_m = 500, list_m = TRUE,ordersprev = list(maxpj = 2))
estim$final_m
# }