Over the past decades, the advent of high-speed electronic computing has drastically altered the practice of applied econometrics by permitting the adoption of specific models of much greater intricacy and of a higher theoretical content than before. The estimation and testing of these models draw heavily on Maximum Likelihood methodology, and practitioners in the field freely use the theorems of this statistical theory along with dispersed results from linear algebra and the canons of numerical maximization. The aim of this book is to set out the main elements of this common basis of so many current empirical studies. To this end I have collected the various parts, adapted them to the particular conditions of econometric analysis, and put them all together in the form of a separate paradigm.

There is a growing realization that some of the basic theorems have not yet been proved with sufficient generality to cover all the cases that arise in econometrics, and much fundamental work is being undertaken to remedy this defect. At the same time, progress is being made in the design of robust methods that may well, in due course, replace Maximum Likelihood as a general method of inference. The present book does not deal with these new developments. It contains nothing new, but some old things that may surprise. While there is progress in econometrics, some of the fashionable novelties have quite old roots, which are generally ignored.