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 Linear Model and Instrumental Variables Estimators. Consistency. Laws of Large Numbers. Asymptotic Normality. Central Limit Theory. Estimating Asymptotic Covariance Matrices. Functional Central Limit Theory and Applications. Directions for Further Study. Solution Set. References. Index.