Conditioning in Dynamic Models

A statistical model is generally defined through a probability on some variables conditionally on other variables and refers to some parameters of interest. Therefore, it seems natural to ask under which conditions such a model does not lose information with respect to a model describing more variables and implying more parameters. Admissibility conditions for reductions by conditioning are investigated both in one‐shot and in dynamic models. By so doing, concepts of ‘exogeneity’ and of ‘non‐causality’ are integrated into a general framework. This paper is essentially a non‐technical introduction to the theory of reduction developed more formally in other papers. It also supplies various examples of the concepts introduced in that theory.