Dynamic error-in-variables models and limited information analysis

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Abstract
A vector stochastic process may be decomposed in to its expectation and a residual process. A linear dynamic model is defined by a set of dynamic linear relations constraining the 's given some conditioning variables and by the distribution of the 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. (This abstract was borrowed from another version of this item.)
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