BiCopGofTest: Goodness-of-Fit Test for Bivariate Copulas
In tnagler/VineCopula: Statistical Inference of Vine Copulas
BiCopGofTest R Documentation
Goodness-of-Fit Test for Bivariate Copulas
Description
This function performs a goodness-of-fit test for bivariate copulas, either
based on White's information matrix equality (White, 1982) as introduced by
Huang and Prokhorov (2011) or based on Kendall's process
(Wang and Wells, 2000; Genest et al., 2006). It computes the test statistics
and p-values.
Usage
BiCopGofTest(
u1,
u2,
family,
par = 0,
par2 = 0,
method = "white",
max.df = 30,
B = 100,
obj = NULL
)
Arguments
u1, u2
Numeric vectors of equal length with values in [0,1].
family
An integer defining the bivariate copula family:
0 = independence copula
1 = Gaussian copula
2 = Student t copula (t-copula) (only for method = "white"; see details)
3 = Clayton copula
4 = Gumbel copula
5 = Frank copula
6 = Joe copula
7 = BB1 copula (only for method = "kendall")
8 = BB6 copula (only for method = "kendall")
9 = BB7 copula (only for method = "kendall")
10 = BB8 copula (only for method ="kendall")
13 = rotated Clayton copula (180 degrees; survival Clayton'') \cr `14` = rotated Gumbel copula (180 degrees; survival Gumbel”)
16 = rotated Joe copula (180 degrees; survival Joe'') \cr `17` = rotated BB1 copula (180 degrees; survival BB1”;
only for method = "kendall")
18 = rotated BB6 copula (180 degrees; survival BB6''; only for `method = "kendall"`)\cr `19` = rotated BB7 copula (180 degrees; survival BB7”;
only for method = "kendall")
20 = rotated BB8 copula (180 degrees; “survival BB8”;
only for method = "kendall")
'23' = rotated Clayton copula (90 degrees)
'24' = rotated Gumbel copula (90 degrees)
'26' = rotated Joe copula (90 degrees)
'27' = rotated BB1 copula (90 degrees; only for 'method = "kendall"')
'28' = rotated BB6 copula (90 degrees; only for 'method = "kendall"')
'29' = rotated BB7 copula (90 degrees; only for 'method = "kendall"')
'30' = rotated BB8 copula (90 degrees; only for 'method = "kendall"')
'33' = rotated Clayton copula (270 degrees)
'34' = rotated Gumbel copula (270 degrees)
'36' = rotated Joe copula (270 degrees)
'37' = rotated BB1 copula (270 degrees; only for 'method = "kendall"')
'38' = rotated BB6 copula (270 degrees; only for 'method = "kendall"')
'39' = rotated BB7 copula (270 degrees; only for 'method = "kendall"')
'40' = rotated BB8 copula (270 degrees; only for 'method = "kendall"')
par
Copula parameter (optional).
par2
Second parameter for bivariate t-copula (optional); default:
par2 = 0.
method
A string indicating the goodness-of-fit method:
"white" = goodness-of-fit test based on White's information matrix
equality (default)
"kendall" = goodness-of-fit test based on Kendall's process
max.df
Numeric; upper bound for the estimation of the degrees of
freedom parameter of the t-copula (default: max.df = 30).
B
Integer; number of bootstrap samples (default: B = 100).
For B = 0 only the the test statistics are returned.
WARNING: If
B is chosen too large, computations will take very long.
obj
BiCop object containing the family and parameter
specification.
Details
method = "white":
This goodness-of fit test uses the information
matrix equality of White (1982) and was investigated by Huang and Prokhorov
(2011). The main contribution is that under correct model specification the
Fisher Information can be equivalently calculated as minus the expected
Hessian matrix or as the expected outer product of the score function. The
null hypothesis is
H_0: \boldsymbol{H}(\theta) +
\boldsymbol{C}(\theta) = 0
against the alternative
H_0:
\boldsymbol{H}(\theta) + \boldsymbol{C}(\theta) \neq 0 ,
where
\boldsymbol{H}(\theta) is the expected Hessian matrix and
\boldsymbol{C}(\theta) is the expected outer product of the score
function. For the calculation of the test statistic we use the consistent
maximum likelihood estimator \hat{\theta} and the sample counter parts
of \boldsymbol{H}(\theta) and \boldsymbol{C}(\theta). The
correction of the covariance-matrix in the test statistic for the
uncertainty in the margins is skipped. The implemented tests assumes that
where is no uncertainty in the margins. The correction can be found in Huang
and Prokhorov (2011). It involves two-dimensional integrals.
WARNING: For
the t-copula the test may be unstable. The results for the t-copula
therefore have to be treated carefully.
method = "kendall":
This copula goodness-of-fit test is based on Kendall's process as
proposed by Wang and Wells (2000). For computation of p-values, the
parametric bootstrap described by Genest et al. (2006) is used. For
rotated copulas the input arguments are transformed and the goodness-of-fit
procedure for the corresponding non-rotated copula is used.
Value
For method = "white":
p.value
Asymptotic p-value.
statistic
The observed test statistic.
For method ="kendall"
p.value.CvM
Bootstrapped p-value of the goodness-of-fit
test using the Cramer-von Mises statistic (if B > 0).
p.value.KS
Bootstrapped p-value of the goodness-of-fit test using the
Kolmogorov-Smirnov statistic (if B > 0).
statistic.CvM
The
observed Cramer-von Mises test statistic.
statistic.KS
The observed
Kolmogorov-Smirnov test statistic.
Author(s)
Ulf Schepsmeier, Wanling Huang, Jiying Luo, Eike Brechmann
References
Huang, W. and A. Prokhorov (2014). A goodness-of-fit test for copulas.
Econometric Reviews, 33 (7), 751-771.
Wang, W. and M. T. Wells (2000). Model selection and semiparametric
inference for bivariate failure-time data. Journal of the American
Statistical Association, 95 (449), 62-72.
Genest, C., Quessy, J. F., and Remillard, B. (2006). Goodness-of-fit
Procedures for Copula Models Based on the Probability Integral Transformation.
Scandinavian Journal of Statistics, 33(2), 337-366.
Luo J. (2011). Stepwise estimation of D-vines with arbitrary specified
copula pairs and EDA tools. Diploma thesis, Technische Universitaet
Muenchen.
https://mediatum.ub.tum.de/?id=1079291.
White, H. (1982) Maximum likelihood estimation of misspecified models,
Econometrica, 50, 1-26.
See Also
BiCopDeriv2(), BiCopDeriv(),
BiCopIndTest(), BiCopVuongClarke()
Examples
# simulate from a bivariate Clayton copula
simdata <- BiCopSim(100, 3, 2)
u1 <- simdata[,1]
u2 <- simdata[,2]
# perform White's goodness-of-fit test for the true copula
BiCopGofTest(u1, u2, family = 3)
# perform White's goodness-of-fit test for the Frank copula
BiCopGofTest(u1, u2, family = 5)
# perform Kendall's goodness-of-fit test for the true copula
BiCopGofTest(u1, u2, family = 3, method = "kendall", B=50)
# perform Kendall's goodness-of-fit test for the Frank copula
BiCopGofTest(u1, u2, family = 5, method = "kendall", B=50)
tnagler/VineCopula documentation built on March 6, 2024, 5 a.m.
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| BiCopGofTest | R Documentation |
Goodness-of-Fit Test for Bivariate Copulas
Description
This function performs a goodness-of-fit test for bivariate copulas, either based on White's information matrix equality (White, 1982) as introduced by Huang and Prokhorov (2011) or based on Kendall's process (Wang and Wells, 2000; Genest et al., 2006). It computes the test statistics and p-values.
Usage
BiCopGofTest(
u1,
u2,
family,
par = 0,
par2 = 0,
method = "white",
max.df = 30,
B = 100,
obj = NULL
)
Arguments
u1, u2 |
Numeric vectors of equal length with values in |
family |
An integer defining the bivariate copula family: |
par |
Copula parameter (optional). |
par2 |
Second parameter for bivariate t-copula (optional); default:
|
method |
A string indicating the goodness-of-fit method: |
max.df |
Numeric; upper bound for the estimation of the degrees of
freedom parameter of the t-copula (default: |
B |
Integer; number of bootstrap samples (default: |
obj |
|
Details
method = "white":
This goodness-of fit test uses the information
matrix equality of White (1982) and was investigated by Huang and Prokhorov
(2011). The main contribution is that under correct model specification the
Fisher Information can be equivalently calculated as minus the expected
Hessian matrix or as the expected outer product of the score function. The
null hypothesis is
H_0: \boldsymbol{H}(\theta) +
\boldsymbol{C}(\theta) = 0
against the alternative
H_0:
\boldsymbol{H}(\theta) + \boldsymbol{C}(\theta) \neq 0 ,
where
\boldsymbol{H}(\theta) is the expected Hessian matrix and
\boldsymbol{C}(\theta) is the expected outer product of the score
function. For the calculation of the test statistic we use the consistent
maximum likelihood estimator \hat{\theta} and the sample counter parts
of \boldsymbol{H}(\theta) and \boldsymbol{C}(\theta). The
correction of the covariance-matrix in the test statistic for the
uncertainty in the margins is skipped. The implemented tests assumes that
where is no uncertainty in the margins. The correction can be found in Huang
and Prokhorov (2011). It involves two-dimensional integrals.
WARNING: For
the t-copula the test may be unstable. The results for the t-copula
therefore have to be treated carefully.
method = "kendall":
This copula goodness-of-fit test is based on Kendall's process as
proposed by Wang and Wells (2000). For computation of p-values, the
parametric bootstrap described by Genest et al. (2006) is used. For
rotated copulas the input arguments are transformed and the goodness-of-fit
procedure for the corresponding non-rotated copula is used.
Value
For method = "white":
p.value |
Asymptotic p-value. |
statistic |
The observed test statistic. |
For method ="kendall"
p.value.CvM |
Bootstrapped p-value of the goodness-of-fit
test using the Cramer-von Mises statistic (if |
p.value.KS |
Bootstrapped p-value of the goodness-of-fit test using the
Kolmogorov-Smirnov statistic (if |
statistic.CvM |
The observed Cramer-von Mises test statistic. |
statistic.KS |
The observed Kolmogorov-Smirnov test statistic. |
Author(s)
Ulf Schepsmeier, Wanling Huang, Jiying Luo, Eike Brechmann
References
Huang, W. and A. Prokhorov (2014). A goodness-of-fit test for copulas. Econometric Reviews, 33 (7), 751-771.
Wang, W. and M. T. Wells (2000). Model selection and semiparametric inference for bivariate failure-time data. Journal of the American Statistical Association, 95 (449), 62-72.
Genest, C., Quessy, J. F., and Remillard, B. (2006). Goodness-of-fit
Procedures for Copula Models Based on the Probability Integral Transformation.
Scandinavian Journal of Statistics, 33(2), 337-366.
Luo J. (2011). Stepwise estimation of D-vines with arbitrary specified
copula pairs and EDA tools. Diploma thesis, Technische Universitaet
Muenchen.
https://mediatum.ub.tum.de/?id=1079291.
White, H. (1982) Maximum likelihood estimation of misspecified models, Econometrica, 50, 1-26.
See Also
BiCopDeriv2(), BiCopDeriv(),
BiCopIndTest(), BiCopVuongClarke()
Examples
# simulate from a bivariate Clayton copula
simdata <- BiCopSim(100, 3, 2)
u1 <- simdata[,1]
u2 <- simdata[,2]
# perform White's goodness-of-fit test for the true copula
BiCopGofTest(u1, u2, family = 3)
# perform White's goodness-of-fit test for the Frank copula
BiCopGofTest(u1, u2, family = 5)
# perform Kendall's goodness-of-fit test for the true copula
BiCopGofTest(u1, u2, family = 3, method = "kendall", B=50)
# perform Kendall's goodness-of-fit test for the Frank copula
BiCopGofTest(u1, u2, family = 5, method = "kendall", B=50)
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