2022/03/09 by Aristidis K. Nikoloulopoulos, Nikoloulopoulos, Aristidis K. · 1 citation
Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2203.04619
openalex publication_date 2022/03/09 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
The composite likelihood (CL) is amongst the computational methods used for the estimation of high-dimensional multivariate normal (MVN) copula models with discrete responses. Its computational advantage, as a surrogate likelihood method, is that is based on the independence likelihood for the univariate regression and non-regression parameters and pairwise likelihood for the correlation parameters, but the efficiency of estimating the univariate regression and non-regression parameters can be low. For a high-dimensional discrete response, we propose weighted versions of the composite likelihood estimating equations and an iterative approach to determine good weight matrices. The general methodology is applied to the MVN copula with univariate ordinal regressions as the marginals. Efficiency calculations show that our method is nearly as efficient as the maximum likelihood for fully specified MVN copula models. Illustrations include simulations and real data applications regarding longitudinal (low-dimensional) and time (high-dimensional) series ordinal response data with covariates and it is shown that there is a substantial gain in efficiency via the weighted CL method.