Dynamic Error-in-Variables Models and Limited Information Analysis

Abstract

A vector stochastic process xt may be decomposed in to its expectations ξt and a residual process vt. A linear dynamic model is defined by a set of dynamic linear relations constraining the ξt S given some conditioning variables and by the distribution of the Vt process. This paper presents a strategy for the specification of this class of models providing computable posterior distributions for a suitable class of prior measures. Some conditional independence properties characterizing exogeneity conditions through global or sequential cuts, innovation property or non causality relations are studied and are shown to allow reductions by conditioning of the model.