ClippedCov-class function

Class to calculate copula covariances from a time series with given levels.Calculates for each combination of levels (tau1,tau2)(tau1,tau2)and for all k<maxLagk<maxLag the copula covariances Cov(IndX0<tau1,IndXk<tau2)Cov(Ind{X0<tau1},Ind{Xk<tau2})and writes it to values[k] from its superclass LagOperator.

Class to calculate copula covariances from a time series with given levels.

Calculates for each combination of levels (tau1,tau2)(tau1,tau2)

and for all k<maxLagk<maxLag the copula covariances Cov(IndX0<tau1,IndXk<tau2)Cov(Ind{X0<tau1},Ind{Xk<tau2})

and writes it to values[k] from its superclass LagOperator.

For each lag k = 0, ..., maxLag and combination of levels (tau1,tau2)(tau1, tau2) from levels.1 x levels.2 the statistic [REMOVE_ME]1nt=1nk(I{F^n(Yt)τ1}τ1)(I{F^n(Yt+k)τ2}τ2)[REMOVEME2] \frac{1}{n} \sum_{t=1}^{n-k} ( I\{\hat F_n(Y_t) \leq \tau_1\} - \tau_1) ( I\{\hat F_n(Y_{t+k}) \leq \tau_2\} - \tau_2) [REMOVE_ME_2]

is determined and stored to the array values. class

Description

For each lag k = 0, ..., maxLag and combination of levels (tau1,tau2)(tau1, tau2) from levels.1 x levels.2 the statistic

1nt=1nk(I{F^n(Yt)τ1}τ1)(I{F^n(Yt+k)τ2}τ2) \frac{1}{n} \sum_{t=1}^{n-k} ( I\{\hat F_n(Y_t) \leq \tau_1\} - \tau_1) ( I\{\hat F_n(Y_{t+k}) \leq \tau_2\} - \tau_2)

is determined and stored to the array values.

Details

Currently, the implementation of this class allows only for the analysis of univariate time series.

  • Maintainer: Tobias Kley
  • License: GPL (>= 2)
  • Last published: 2024-07-11