We present a family of smooth tests for the goodness of fit of semiparametric multivariate copula models. The proposed tests are distribution free and can be easily implemented. They are diagnostic and constructive in the sense that when a null distribution is rejected, the test provides useful pointers to alternative copula distributions. We then propose a method of copula density construction, which can be viewed as a multivariate extension of Efron and Tibshirani (1996). We further generalize our methods to dynamic copula models of Chen and Fan (2006). We report extensive Monte Carlo simulations and three empirical examples to illustrate the effectiveness and usefulness of our method
We study a test statistic based on the integrated squared difference between a kernel estimator of t...
We study a test statistic based on the integrated squared difference between a kernel estimator of t...
We propose a new rank-based goodness-of-fit test for copulas. It uses the information matrix equalit...
We present a family of smooth tests for the goodness of fit of semiparametric multivariate copula mo...
We propose a family of smooth tests of copula specification of multivariate copula models under gene...
We propose a family of data-driven tests for the goodness-of-fit of copula-based multivariate surviv...
AbstractThis paper defines two distribution free goodness-of-fit test statistics for copulas. It sta...
Consider a random sample from a continuous multivariate distribution function F with copula C. In or...
Consider a random sample from a continuous multivariate distribution function F with copula C. In or...
A new goodness-of-fit test of copulas is proposed. It is based on restrictions on certain elements o...
Copulas have been known in the statistical literature for many years, and have become useful tools ...
Copulas are used to model multivariate data as they account for the dependence structure and provide...
A new goodness-of-fit test for copulas is proposed. It is based on restrictions on certain elements ...
In recent years, stationary time series models based on copula functions became increasingly popular...
AbstractWe study a test statistic based on the integrated squared difference between a kernel estima...
We study a test statistic based on the integrated squared difference between a kernel estimator of t...
We study a test statistic based on the integrated squared difference between a kernel estimator of t...
We propose a new rank-based goodness-of-fit test for copulas. It uses the information matrix equalit...
We present a family of smooth tests for the goodness of fit of semiparametric multivariate copula mo...
We propose a family of smooth tests of copula specification of multivariate copula models under gene...
We propose a family of data-driven tests for the goodness-of-fit of copula-based multivariate surviv...
AbstractThis paper defines two distribution free goodness-of-fit test statistics for copulas. It sta...
Consider a random sample from a continuous multivariate distribution function F with copula C. In or...
Consider a random sample from a continuous multivariate distribution function F with copula C. In or...
A new goodness-of-fit test of copulas is proposed. It is based on restrictions on certain elements o...
Copulas have been known in the statistical literature for many years, and have become useful tools ...
Copulas are used to model multivariate data as they account for the dependence structure and provide...
A new goodness-of-fit test for copulas is proposed. It is based on restrictions on certain elements ...
In recent years, stationary time series models based on copula functions became increasingly popular...
AbstractWe study a test statistic based on the integrated squared difference between a kernel estima...
We study a test statistic based on the integrated squared difference between a kernel estimator of t...
We study a test statistic based on the integrated squared difference between a kernel estimator of t...
We propose a new rank-based goodness-of-fit test for copulas. It uses the information matrix equalit...