Choose_pk {mixAR}

Choose the autoregressive order of MixAR components

Description

Reversible Jump MCMC algorithm to choose the optimal autoregressive order of each component of a mixture autoregressive model.

Usage

Choose_pk(y, model, fix_shift = FALSE, tau, pmax, method, par = NULL, nsim)

Arguments

y

a time series. Currently a numeric vector.

model

an object inheriting from class "MixAR".

fix_shift

whether the shift/mean parameter should be kept fixed to its starting value or not. Default is FALSE.

tau

tuning parameters for Metropolis-Hastings algorithm in sampling AR coefficients.

pmax

the largest autoregressive order allowed for each component.

method

character vector of length 1. Method for calculating probability of new AR order to be increased/decreased by 1 unit. Currently available "Ratio", "Poisson" and "NULL". Default is "NULL".

par

numeric, parameter for tuning probabilities according to method. Ignored if method is "NULL".

nsim

numeric, the number of iterations.

Value

out

a dataframe with g+1 columns. The first g columns contain the autoregressive orders of the components, the last column how often a model is preferred, divided by nsim.

fix_shift

the choice made for the shift/mean parameters.

method

the method used to increase/decrease AR orders.

Note

Choose_pk currenlty supports class "MixARGaussian" only.

Author(s)

Davide Ravagli

References

Ravagli D, Boshnakov GN (2020). “Bayesian analysis of mixture autoregressive models covering the complete parameter space.” 2006.11041, https://arxiv.org/abs/2006.11041.

See Also

bx_dx for more details on the method

Examples

model <- new("MixARGaussian", 
             prob   = exampleModels$WL_At@prob,      # c(0.5, 0.5)
             scale  = exampleModels$WL_At@scale,     # c(1, 2)        
             arcoef = list(-0.5, 1) )
             # note: arcoef != list(-0.5, 1.1) == exampleModels$WL_At@arcoef@a

set.seed(1234)
n <- 50 # 200
y <- mixAR_sim(model, n, rep(0, max(model@order)), nskip = 100)

nsim <- 25 # 100
pk_star <- Choose_pk(y, model, tau = c(.15, .25), pmax = 5, method = "NULL", nsim = nsim)