BiCopVuongClarke(u1, u2, familyset=NA, correction=FALSE, level=0.05)familyset = NA (default), all possible families are compared.
Possible families are:
0 = independence copula
1 = Gaussian copula
2 = Student t copula (t-copula)
3 = Clayton copula
4 = Gumbel copula
5 = Frank copula
6 = Joe copula
7 = BB1 copula
8 = BB6 copula
9 = BB7 copula
10 = BB8 copula
13 = rotated Clayton copula (180 degrees; ``survival Clayton'')
14 = rotated Gumbel copula (180 degrees; ``survival Gumbel'')
16 = rotated Joe copula (180 degrees; ``survival Joe'')
17 = rotated BB1 copula (180 degrees; ``survival BB1'')
18 = rotated BB6 copula (180 degrees; ``survival BB6'')
19 = rotated BB7 copula (180 degrees; ``survival BB7'')
20 = rotated BB8 copula (180 degrees; ``survival BB8'')
23 = rotated Clayton copula (90 degrees)
24 = rotated Gumbel copula (90 degrees)
26 = rotated Joe copula (90 degrees)
27 = rotated BB1 copula (90 degrees)
28 = rotated BB6 copula (90 degrees)
29 = rotated BB7 copula (90 degrees)
30 = rotated BB8 copula (90 degrees)
33 = rotated Clayton copula (270 degrees)
34 = rotated Gumbel copula (270 degrees)
36 = rotated Joe copula (270 degrees)
37 = rotated BB1 copula (270 degrees)
38 = rotated BB6 copula (270 degrees)
39 = rotated BB7 copula (270 degrees)
40 = rotated BB8 copula (270 degrees)
correction = FALSE (no correction; default), "Akaike" and "Schwarz".level = 0.05).CDVineVuongTest and CDVineClarkeTest for descriptions of the two tests).
In the goodness-of-fit test proposed by Belgorodski (2010) this is used for bivariate copula selection.
It compares a model 0 to all other possible models under consideration.
If model 0 is favored over another model, a score of "+1" is assigned and similarly a score of "-1" if the other model is determined to be superior.
No score is assigned, if the respective test cannot discriminate between two models.
Both tests can be corrected for the numbers of parameters used in the copulas.
Either no correction (correction = FALSE), the Akaike correction (correction = "Akaike")
or the parsimonious Schwarz correction (correction = "Schwarz") can be used.The models compared here are bivariate parametric copulas and we would like to determine which family fits the data better than the other families.
E.g., if we would like to test the hypothesis that the bivariate Gaussian copula fits the data best, then we
compare the Gaussian copula against all other copulas under consideration.
In doing so, we investigate the null hypothesis "The Gaussian copula fits the data better than all other copulas under consideration",
which corresponds to $k-1$ times the hypothesis "The Gaussian copula $C_j$ fits the data better than copula $C_i$" for all $i=1,...,k, i!=j$, where $k$ is the number
of bivariate copula families under consideration (length of familyset).
This procedure is done not only for one family but for all families under consideration, i.e., two scores, one based on the Vuong and one based on the Clarke test,
are returned for each bivariate copula family.
If used as a goodness-of-fit procedure, the family with the highest score should be selected.
For more and detailed information about the goodness-of-fit test see Belgorodski (2010).
Clarke, K. A. (2007). A Simple Distribution-Free Test for Nonnested Model Selection. Political Analysis, 15, 347-363.
Vuong, Q. H. (1989). Ratio tests for model selection and non-nested hypotheses. Econometrica 57 (2), 307-333.
BiCopGofKendall, CDVineVuongTest, CDVineClarkeTest,
BiCopSelect
# simulate from a t-copula
dat = BiCopSim(500,2,0.7,5)
# apply the test for families 1-10
## Not run:
# vcgof = BiCopVuongClarke(dat[,1],dat[,2],familyset=c(1:10))
#
# # display the Vuong test scores
# vcgof[1,]
# ## End(Not run)
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