Pseudo-residuals are defined as the Rosenblatt transform of the data, conditional on the past. Under a correctly specified model, they are approximately iid uniform on \([0, 1]^d\).
Arguments
- x
the data.
- model
model inheriting from class svine_dist.
- cores
number of cores to use; if larger than one, computations are performed in parallel on
coresbatches.
Value
An n-by-d matrix of pseudo-residuals, where
n = NROW(x) - model$copula$p and d is the cross-sectional dimension.
Examples
# load data set
data(returns)
dat <- returns[1:100, 1:3]
# fit parametric S-vine model with Markov order 1
fit <- svine(dat, p = 1, family_set = "parametric")
# compute pseudo-residuals
# (should be independent uniform across variables and time)
v <- svine_pseudo_residuals(dat, fit)
pairs(cbind(v[-1, ], v[-nrow(v), ]))
