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
- u
the data; should have approximately uniform margins.
- model
model inheriting from class svinecop_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(u) - model$p and d is the cross-sectional dimension.
Examples
# load data set
data(returns)
# convert to pseudo observations with empirical cdf for marginal distributions
u <- pseudo_obs(returns[1:100, 1:3])
# fit parametric S-vine copula model with Markov order 1
fit <- svinecop(u, p = 1, family_set = "parametric")
# compute pseudo-residuals
# (should be independent uniform across variables and time)
v <- svinecop_pseudo_residuals(u, fit)
pairs(cbind(v[-1, ], v[-nrow(v), ]))
