Under normality, the Bayesian estimation problem, the best linear unbiased estimation problem, and the restricted least-squares problem are all equivalent. As a result we need not compute pseudo-inverses and other complicated functions, which will be impossible for large sparse systems. Instead,...

Sensitivity analysis is important for its own sake and also in combination with diagnostic testing. We consider the question how to use sensitivity statistics in practice, in particular how to judge whether sensitivity is large or small. For this purpose we distinguish between absolute and...

We present a Bayesian estimation method applied to an extended set of national accounts data and estimates of approximately 2500 variables. The method is based on conventional national accounts frameworks as compiled by countries in Central America, in particular Guatemala, and on concepts that...