Description Arguments Details Value Author(s) References See Also Examples

The function can be used to test for the partial copula and the simplifying assumption for a bivariate conditional copula using different tests. Two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are implemented. For all tests different options can be set by generating a pacotest options list using the `pacotestset`

function.

`U` |
A (n x 2) matrix of [0,1] data (probability integral transforms), which are the arguments of the conditional copula of (Y,Z)|W for which the simplifying assumption should be tested. The first column is given by the conditional distribution function of Y|W evaluated at the observed values of Y and W. Analogously, the second column is defined as the conditional distribution function of Z|W evaluated at the observed values of Z and W. If the probability integral transforms are obtained from the partial vine copula (PVC), i.e., partial probability integral transforms (PPITs) are used, the function can be used to test for (j-1)-th order partial copulas. |

`W` |
A (n x K) matrix of observed values for the vector of random variables on which the conditioning is done. |

`pacotestOptions` |
A options list generated by the |

Applying a test with default options (cf. `pacotestset`

) and with known (i.e., not estimated) PITs (probability integral transforms) in `U`

.

1 | ```
out = pacotest(U,W,'CCC')
``` |

1 | ```
out = pacotest(U,W,'VI')
``` |

Applying a test with options specified in `pacotestOptions`

1 | ```
out = pacotest(U,W,pacotestOptions)
``` |

Note that when calling `pacotest(U,W,'CCC')`

, the default options for the CCC test are used (cf. `pacotestset`

), but the two parameters `withEstUncert = FALSE`

and `estUncertWithRanks = FALSE`

are altered. In contrast when calling `pacotestOptions = pacotestset('CCC')`

, the two parameters are set to `withEstUncert = TRUE`

and `estUncertWithRanks = TRUE`

. For the CCC test, under the default setting, it is assumed that estimated PPITs are provided and the test statistic is computed under consideration of estimation uncertainty of the probability integral transforms, i.e., `withEstUncert = TRUE`

and `estUncertWithRanks = TRUE`

. To apply `pacotest`

with `withEstUncert = TRUE`

, three additional inputs have to be provided (`data`

, `svcmDataFrame`

and `cPitData`

).

In the vine copula context, PPITs are usually estimated and not known. Therefore, in the vine copula context it is recommended to use the functions `pacotestRvineSeq`

or `pacotestRvineSingleCopula`

instead of `pacotest`

. These functions automatically pass through the additional arguments `data`

, `svcmDataFrame`

, `cPitData`

to the function `pacotest`

and the CCC test can be applied in its default setting with consideration of estimation uncertainty of the probability integral transforms, i.e., `withEstUncert = TRUE`

and `estUncertWithRanks = TRUE`

.

A list which can, depending on the chosen test, consist of the following elements:

`pValue` |
The p-value of the test. |

`testStat` |
The value of the test statistic. |

`decisionTree` |
The decision tree used to partition the support Lambda0 of the conditioning variable W. It is provided as a list consisting of three nodes ( |

`S` |
The bootstrapped values of the test statistic (only for the test type |

Malte S. Kurz

Kurz, M. S. and F. Spanhel (2017), "Testing the simplifying assumption in high-dimensional vine copulas", ArXiv e-prints https://arxiv.org/abs/1706.02338.

Spanhel, F. and M. S. Kurz (2019), "Simplified vine copula models: Approximations based on the simplifying assumption", Electronic Journal of Statistics 13 (1), 1254-1291.

Spanhel, F. and M. S. Kurz (2016), "The partial copula: Properties and associated dependence measures", Statistics & Probability Letters 119, pp. 76-83.

`pacotest-package`

, `pacotestset`

, `pacotestRvineSeq`

, `pacotestRvineSingleCopula`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | ```
#####################
# Generate an options list, e.g., the constant conditional correlation (CCC)
# test with default options. We use known PITs and don't estimate the parameters
# in the lower trees of the vine copulas and therefore additionally alter the
# two parameters withEstUncert and estUncertWithRanks to FALSE.
pacotestOptions=pacotestset(testType='CCC', withEstUncert = FALSE, estUncertWithRanks = FALSE)
#####################
# Use the specified options to test for the simplifying assumption
##### Example 1: Non-simplified three-dim. C-Vine #####
# Simulate from a three-dimensional C-Vine copula with C_12 and C_13
# being product copulas and C_23|1 being a Frank copula with
# functional parameter theta(x_{1}) = (4x_{1}-2)^3
N = 500
X = matrix(runif(3*N),N,3)
theta = (4*X[,1]-2)^3
etheta = expm1(-theta);
X[,3] = -1/theta*log(1+etheta/(exp(-theta*X[,2])*(1/X[,3]-1)+1));
Result = pacotest(X[,c(2,3)],X[,1],pacotestOptions)
Result$pValue
##### Example 2: Non-simplified three-dim. C-Vine #####
# Simulate from a three-dimensional C-Vine copula with C_12 and C_13
# being product copulas and C_23|1 being a Frank copula with
# functional parameter theta(x_{1}) = 12 + 8*sin(0.4(3x_{1}+2)^2)
X = matrix(runif(3*N),N,3)
theta = 12 + 8*sin(0.4*(3*X[,1]+2)^2)
etheta = expm1(-theta);
X[,3] = -1/theta*log(1+etheta/(exp(-theta*X[,2])*(1/X[,3]-1)+1));
Result = pacotest(X[,c(2,3)],X[,1],pacotestOptions)
Result$pValue
##### Example 3: Simplified three-dim. C-Vine #####
# Simulate from a three-dimensional C-Vine copula with C_12 and C_13
# being Clayton copulas with parameter theta and C_23|1 being a Clayton copula with
# functional parameter theta(x_{1}) = theta / (1+theta)
W = matrix(runif(3*N),N,3)
X = matrix(NA,N,3)
theta = 2
X[,1] = W[,1]
X[,2] = (W[,1]^(-theta)*(W[,2]^((-theta)/(1+theta))-1)+1)^(-1/theta);
theta_23_1 = theta /(1+theta)
X[,3] = (W[,2]^(-theta_23_1)*(W[,3]^((-theta_23_1)/(1+theta_23_1))-1)+1)^(-1/theta_23_1);
X[,3] = (W[,1]^(-theta)*(X[,3]^((-theta)/(1+theta))-1)+1)^(-1/theta);
# Get pseudo-observations from the conditional copula C_23|1
U = matrix(NA,N,2)
U[,1] = (X[,1]^theta*(X[,2]^(-theta)-1)+1)^(-(1+theta)/theta);
U[,2] = (X[,1]^theta*(X[,3]^(-theta)-1)+1)^(-(1+theta)/theta);
Result = pacotest(U,X[,1],pacotestOptions)
Result$pValue
``` |

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