Watanabe-Akaike or Widely Available Information Criterion (WAIC) for objects of class mtar

This function computes the Watanabe-Akaike or Widely Available Information Criterion (WAIC), for objects of class mtar.

Usage

# S3 method for class 'mtar'
WAIC(...)

Arguments

...

one or several objects of the class mtar.

Value

A numeric matrix containing the WAIC values corresponding to each mtar object in the input.

See also

DIC

Examples

# \donttest{
###### Example 1: Returns of the closing prices of three financial indexes
data(returns)
fit1 <- mtar_grid(~ COLCAP + BOVESPA | SP500, data=returns, row.names=Date,
                  subset={Date<="2015-12-07"}, dist=c("Gaussian","Student-t",
                  "Slash","Laplace"), nregim.min=2, nregim.max=3, p.min=2,
                  p.max=2, n.burnin=1000, n.sim=2000, n.thin=2,
                  plan_strategy="multisession")
WAIC(fit1)
#>                    WAIC
#> Gaussian.2.2  -17913.53
#> Gaussian.3.2  -18154.69
#> Laplace.2.2   -17941.61
#> Laplace.3.2   -18116.24
#> Slash.2.2     -18031.85
#> Slash.3.2     -18193.73
#> Student-t.2.2 -18044.03
#> Student-t.3.2 -18256.47

###### Example 2: Rainfall and two river flows in Colombia
data(riverflows)
fit2 <- mtar_grid(~ Bedon + LaPlata | Rainfall, data=riverflows,
                  row.names=Date, subset={Date<="2009-02-13"},dist="Laplace",
                  nregim.min=2, nregim.max=3, p.min=1, p.max=3,n.burnin=1000,
                  n.sim=2000, n.thin=2, plan_strategy="multisession")
WAIC(fit2)
#>                 WAIC
#> Laplace.2.1 13124.75
#> Laplace.2.2 13090.06
#> Laplace.2.3 13073.62
#> Laplace.3.1 13069.83
#> Laplace.3.2 13043.65
#> Laplace.3.3 13031.03

###### Example 3: Temperature, precipitation, and two river flows in Iceland
data(iceland.rf)
fit3 <- mtar_grid(~ Jokulsa + Vatnsdalsa | Temperature | Precipitation,
                  data=iceland.rf,subset={Date<="1974-11-06"},row.names=Date,
                  dist=c("Slash","Student-t"), nregim.min=1, nregim.max=2,
                  p.min=15, p.max=15, q.min=4, q.max=4, d.min=2, d.max=2,
                  n.burnin=1000, n.sim=2000, n.thin=2,
                  plan_strategy="multisession")
WAIC(fit3)
#>                        WAIC
#> Slash.1.15.4.2     8307.968
#> Slash.2.15.4.2     7638.291
#> Student-t.1.15.4.2 8417.993
#> Student-t.2.15.4.2 7684.631

###### Example 4: U.S. stock returns
data(US.returns)
fit4 <- mtar_grid(~ CCR | dVIX, data=US.returns, subset={Date<="2025-11-28"},
                  row.names=Date, dist=c("Laplace","Student-t","Slash"),
                  nregim.min=2, nregim.max=2, p.min=3, p.max=3, d.min=3,
                  d.max=3, n.burnin=1000, n.sim=2000, n.thin=2,
                  plan_strategy="multisession")
WAIC(fit4)
#>                     WAIC
#> Laplace.2.3.3   14984.56
#> Slash.2.3.3     15027.93
#> Student-t.2.3.3 14949.57
# }