Official Statistics Data Integration Using Copulas

Abstract

The aim of this paper is to propose a novel approach to integrate financial information, incorporating the dependence structure among the variables in the model. The approach is based on two types of graphical models: vines and non-parametric Bayesian belief nets (NPBBNs). Vines are undirected graphs, representing pair copula constructions, which are used to model the dependence structure of a set of variables. NPBBNs are directed graphs, that use pair copulas to model the dependencies, and allow US for diagnosis and prediction via conditionalization. This approach permits to aggregate information and to calibrate the results obtained with different sources of data. The illustrated methodologies are applied to two financial datasets, the first one containing data collected through a survey and the second one containing official statistics data.