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Is there a goodness of fit test for copulas?
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.
How to get approximate p-values for copulas?
In that case, approximate p-values for the test statistic can be obtained either using a parametric bootstrap (see references two and three) or by means of a faster multiplier approach (see references four and five). Alternative test statistics can be used, in particular if a parametric bootstrap is employed.
What are the results of a copula blanket test?
They describe and interpret the results of a large Monte Carlo experiment designed to assess the effect of the sample size and the strength of dependence on the level and power of the blanket tests for various combinations of copula models under the null hypothesis and the alternative.
When to use a parametric test for a copula?
Only in exceptional cases should this be different from the default test.method = “family” . If test.method = “single”, a test precisely for the provided copula (not its parametric family) is conducted. This makes sense only for specific applications such as testing random number generators.
How is the goodness of fit test calculated?
Bootstrapped p-value of the goodness-of-fit test using the Kolmogorov-Smirnov statistic (if B > 0 ). The observed Cramer-von Mises test statistic. The observed Kolmogorov-Smirnov test statistic. This goodness-of fit test uses the information matrix equality of White (1982) and was investigated by Huang and Prokhorov (2011).
Which is the best book for parametric copulas?
The books of Joe (1997) and Nelsen (2006) provide handy compendiums of the most common parametric families of copulas.
https://www.youtube.com/watch?v=VKQJtaY1ts0