Nothing
test_that("dcc constant residuals",{
stdize <- FALSE
type <- "standard"
r <- coredata(residuals(global_dcc_constant_estimate, standardize = stdize, type = type))
expect_equal(as.numeric(r), as.numeric(global_dcc_constant_estimate$spec$target$y))
stdize <- TRUE
r <- coredata(residuals(global_dcc_constant_estimate, standardize = stdize, type = type))
sd_r <- apply(r, 2, sd)
expect_equal(mean(sd_r), 1.0, tolerance = 0.02)
})
test_that("dcc dynamic residuals",{
stdize <- FALSE
type <- "standard"
r <- coredata(residuals(global_dcc_dynamic_estimate, standardize = stdize, type = type))
expect_equal(as.numeric(r), as.numeric(global_dcc_dynamic_estimate$spec$target$y))
stdize <- TRUE
r <- coredata(residuals(global_dcc_dynamic_estimate, standardize = stdize, type = type))
# these are the same as the constant case since standardization is on the univariate residuals (same model)
sd_r <- apply(r, 2, sd)
expect_equal(mean(sd_r), 1.0, tolerance = 0.02)
})
test_that("dcc constant filter no update",{
global_dcc_constant_filter <- tsfilter(global_dcc_constant_estimate, y = y[test_index,test_series], update = FALSE)
expect_equal(residuals(global_dcc_constant_estimate, standardize = TRUE), residuals(global_dcc_constant_filter, standardize = TRUE)[sample_index,], tolerance = 1e-5)
})
test_that("dcc dynamic filter no update",{
global_dcc_dynamic_filter <- tsfilter(global_dcc_dynamic_estimate, y = y[test_index,test_series], update = FALSE)
global_adcc_dynamic_filter <- tsfilter(global_adcc_dynamic_estimate, y = y[test_index,test_series], update = FALSE)
expect_equal(residuals(global_dcc_dynamic_estimate, standardize = TRUE), residuals(global_dcc_dynamic_filter, standardize = TRUE)[sample_index,], tolerance = 1e-5)
expect_equal(residuals(global_adcc_dynamic_estimate, standardize = TRUE), residuals(global_adcc_dynamic_filter, standardize = TRUE)[sample_index,], tolerance = 1e-5)
})
test_that("dcc dynamic prediction",{
n_series <- length(test_series)
h <- 1
nsim <- 10
p <- predict(global_dcc_dynamic_estimate, h = h, nsim = nsim, seed = 100)
V <- tscov(p, distribution = TRUE)
expect_equal(dim(V), c(n_series, n_series, h, nsim))
C <- tscor(p, distribution = TRUE)
expect_equal(dim(C), c(n_series, n_series, h, nsim))
port <- tsaggregate(p, weights = rep(1/n_series, n_series))
expect_equal(dim(port$mu), c(nsim, h))
expect_equal(dim(port$sigma), c(nsim, h))
h <- 3
p <- predict(global_dcc_dynamic_estimate, h = h, nsim = nsim, seed = 100)
V <- tscov(p, distribution = TRUE)
expect_equal(dim(V), c(n_series, n_series, h, nsim))
C <- tscor(p, distribution = TRUE)
expect_equal(dim(C), c(n_series, n_series, h, nsim))
port <- tsaggregate(p, weights = rep(1/n_series, n_series))
expect_equal(dim(port$mu), c(nsim, h))
expect_equal(dim(port$sigma), c(nsim, h))
})
test_that("dcc constant prediction",{
n_series <- length(test_series)
h <- 1
nsim <- 10
p <- predict(global_dcc_constant_estimate, h = h, nsim = nsim, seed = 100)
V <- tscov(p, distribution = TRUE)
expect_equal(dim(V), c(n_series, n_series, h, nsim))
C <- tscor(p, distribution = TRUE)
# constant correlation no distribution
expect_equal(dim(C), c(n_series, n_series))
C <- tscor(p, distribution = FALSE)
# constant correlation no distribution
expect_equal(dim(C), c(n_series, n_series))
port <- tsaggregate(p, weights = rep(1/n_series, n_series))
expect_equal(dim(port$mu), c(nsim, h))
expect_equal(dim(port$sigma), c(nsim, h))
h <- 3
p <- predict(global_dcc_constant_estimate, h = h, nsim = nsim, seed = 100)
V <- tscov(p, distribution = TRUE)
expect_equal(dim(V), c(n_series, n_series, h, nsim))
C <- tscor(p, distribution = TRUE)
expect_equal(dim(C), c(n_series, n_series))
port <- tsaggregate(p, weights = rep(1/n_series, n_series))
expect_equal(dim(port$mu), c(nsim, h))
expect_equal(dim(port$sigma), c(nsim, h))
})
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