Asymptotic Theory for Econometricians

The amount of financial data created every day by world stock markets, world governments, financial institutions, and other sources, is increasing at an enormous rate. Economists and financial analysts need tools to manage these large sets of data in a timely and accurate way. Classical linear models of economics have failed to deal with such large amounts of data, and asymptotic theory is the tool that economists have come to rely on for this type of data management. Large sample theory and the fundamental tools of asymptotic theory converge in this thoroughly revised edition of Asymptotic Theory for Econometricians. New material on functional central limit theory and its applications, material on cointegration, and many small points make this Revised Edition a comprehensive and unified treatment of large sample theory. The scope of the book remains the same as that of the First Edition, with sufficient material to fill a full year's course work. This edition also contains updated material on asymptotically efficient instrumental variables estimation, efficient estimation with estimated error covariance matrices, and efficient IV estimation. Exercise solutions have also been updated and expanded. Asymptotic Theory for Econometricians is intended both as a reference for practicing econometricians and financial analysts and as a textbook for graduate students taking courses in econometrics beyond the introductory level. It assumes that the reader is familiar with the basic concepts of probability and statistics as well as with calculus and linear algebra, and that the reader also has a good understanding of the classical linear model.