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This paper provides a new approach to recover relative entropy measures of contemporaneous dependence from limited information by constructing the most entropic copula (MEC) and its canonical form, namely the most entropic canonical copula (MECC). The MECC can effectively be obtained by maximizing Shannon entropy to yield a proper copula such that known dependence structures of data (e.g., measures of association) are matched to their empirical counterparts. In fact the problem of maximizing the entropy of copulas is the dual to the problem of minimizing the Kullback-Leibler cross entropy (KLCE) of joint probability densities when the marginal probability densities are fixed. Our simulation study shows that the proposed MEC estimator can potentially outperform many other copula estimators in finite samples.

This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized estimating equations models with weakly dependent data. The paper proposes new estimation methods based on smoothed two-step versions of the Generalized Method of Moments and Generalized Empirical Likelihood methods. An important aspect of the paper is that it allows the first step estimation to have an effect on the asymptotic variances of the second-step estimators and explicitly characterizes this effect for the empirically relevant case of the so-called generated regressors. The results of the paper are illustrated with a partially linear model that has not been previously considered in the literature. The proofs of the results utilize a new uniform strong law of large numbers and a new central limit theorem for U -statistics with varying kernels that are of independent interest.

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In this article, a rolling window strategy is used to detect the linear and non‐linear Granger causality relationships between the U.S. federal funds rate and the 10‐year government bond rate, during different time horizons, investigating whether these causalities change with the passing of time. For linear Granger causality tests, we apply the Toda and Yamamoto () approach and for non‐linear ones we use a non‐linear Granger causality test introduced by Diks and Panchenko (). Our findings show that during nearly all time periods there is a significant two‐way Granger causality relationship between these two interest rates.