This paper extends the analysis of bivariate seemingly unrelated (SUR) Tobit model by modeling its nonlinear dependence structure through copulas. The capability in coupling together the different marginal distributions allows the flexible modeling for the SUR Tobit. The ability in capturing...

Copulas are full measures of dependence among random variables. They are increasingly popular among academics and practitioners in financial econometrics for modeling comovements between markets, risk factors, and other relevant variables. A copula's hidden dependence structure that couples a...

Testing weather or not data belongs could been generated by a family of extreme value copulas is difficult. We generalize a test and we prove that it can be applied whatever the alternative hypothesis. We also study the effect of using different extreme value copulas in the context of risk...

Based on the method of copulas, we construct a parametric family of multivariate distributions using mixtures of independent conditional distributions. The new family of multivariate copulas is a convex combination of products of independent and comonotone subcopulas. It fulfills the four most...

This paper presents a novel copula-based autoregressive framework for multilayer arrays of integer-valued time series with tensor structure. It complements recent advances in tensor time series that predominantly focus on real-valued data and overlook the unique properties of integer-valued time...

Chapter 1. Introduction -- Chapter 2. Robust Dynamic Space–time Panel Data Models Using εε-contamination: An Application to Crop Yields and Climate Change -- Chapter 3. Unbiased Estimation of the OLS Covariance Matrix When the Errors are Clustered -- Chapter 4. Refined GMM Estimators for...