THE PURPOSE OF THIS PAPER is to develop the theory of aggregation in a relatively simple and general manner. The theory encompasses linear and nonlinear models, with the results for linear models emerging as special cases of a more general treatment. It was recognized at an early stage of the investigation of aggregation problems that the analysis must be based on implicit function theory [11], [13]; it follows that most of the results have local rather than global validity. However, as one might expect, when specialized to the linear case the results are globally valid. It turns out that the theory of identification in econometric models can be based on precisely the same principles as the theory of aggregation. This presents an interesting dilemma. Theoretical economists on the whole seem to be of the opinion that consistent aggregation, in the sense which is described below, is nearly always impossible to achieve. Practical econometricians invariably assume that their models are (at least partially) identifiable. It is shown in this paper that the possibility of consistent aggregation and the possibility of achieving identification both depend on the same type of condition, namely the existence of a solution to a certain set of linear equations. Of course it is the case that in econometric work assumptions are always made which ensure that the relevant equation system has a solution; these assumptions take the form of the imposition of a priori restrictions to augment the number of linearly independent columns of the matrix of the equation system and thus guarantee that a solution exists.