Asymptotic Theory for Econometricians: Revised Edition (Economic Theory, Econometrics, and Mathematical Economics) (Economic Theory, Econometrics, & Mathematical Economics)

This book provides the tools and concepts necessary to study the behavior of econometric estimators and test statistics in large samples. An econometric estimator is a solution to an optimization problem; that is, a problem that requires a body of techniques to determine a specific solution in a defined set of possible alternatives that best satisfies a selected object function or set of constraints. Thus, this highly mathematical book investigates situations concerning large numbers, in which the assumptions of the classical linear model fail. Economists, of course, face these situations often. It includes completely revised chapter seven on functional central limit theory and its applications, specifically unit root regression, spurious regression, and regression with cointegrated processes. It includes updated material on: central limit theory; asymptotically efficient instrumental variables estimation; estimation of asymptotic covariance matrices; efficient estimation with estimated error covariance matrices; and efficient IV estimation. The Amount Of Financial Data Created Every Day By World Stock Markets, World Governments, Financial Situations, 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. 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.--book Jacket. The Linear Model And Instrumental Variables Estimators -- Consistency -- Laws Of Large Neumbers -- Asymptotic Normality -- Central Limit Theory -- Estimating Asymptotic Covariance Matrices -- Functional Central Limit Theory And Applications -- Directions For Further Study. Halbert White. Includes Bibliographical References And Index. Asymptotic theory allows economists and econometricians to manage random data and variables. Unlike physicists, economists usually cannot conduct controlled experiments; the data they analyze are usually the result of a combination of random events and actions (domestic economic policy, changing world commodity prices, earthquakes, antitrust suits). Unlike classical linear models of economics, asymptotic (or large sample) theory enables economists to account for the randomness of these daily variables and predict market and economic outcomes with greater certainty. The purpose of this book is to provide the reader with the tools and concepts needed to study the behavior of econometric estimators and test statistics in large samples.