<p>ASYMPTOTIC THEORY FOR ECONOMETRICIANS<br/>  <br/>Revised Edition<br/><br/>By <br/>Halbert White, University of California, San Diego, La Jolla, U.S.A.<br/><br/>Description <br/>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.<br/><br/>Audience <br/>The book is intended both as a reference and 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.<br/><br/>Contents <br/>Preface to the First Edition. Preface to the Revised Edition. References.<br/><br/>The Linear Model and Instrumental Variables Estimators: <br/>References. For Further Reading.<br/><br/>Consistency: <br/>Limits. Almost Sure Convergence. Convergence in Probability. Convergence in rth Mean. References.<br/><br/>Laws of Large Numbers: <br/>Independent Identically Distributed Observations. Independent Heterogeneously Distributed Observations. Dependent Identically Distributed Observations. Dependent Heterogeneously Distributed Observations. Martingale Difference Sequences. References.<br/><br/>Asymptotic Normality: <br/>Convergence in Distribution. Hypothesis Testing. Asymptotic Efficiency. References. <br/><br/>Central Limit Theory: <br/>Independent Identically Distributed Observations. Independent Heterogeneously Distributed Observations. Dependent Identically Distributed Observations. Dependent Heterogeneously Distributed Observations. Martingale Difference Sequences. References.<br/><br/>Estimating Asymptotic Covariance Matrices: <br/>General Structure of Vn. Case 1: {Zt xt } Uncorrelated. Case 2: {Zt xt } Finitely Correlated. Case 3: {Zt xt } Asymptotically Uncorrelated. References.<br/><br/>Functional Central Limit Theory and Applications: <br/>Random Walks and Wiener Processes. Weak Convergence. Functional Central Limit Theorems. Regression with a Unit Root. Spurious Regression and Multivariate FCLTs. Cointegration and Stochastic Integrals. References.<br/><br/>Directions for Further Study: <br/>Extending the Data Generating Process. Nonlinear Models. Other Estimation Techniques. Model Misspecication. References. Solution Set. References. Index.<br/><br/>Bibliographic & ordering Information <br/>Hardbound, 264 pages, publication date: OCT-2000<br/>ISBN-13: 978-0-12-746652-1<br/>ISBN-10: 0-12-746652-5<br/>Imprint: ACADEMIC PRESS</p><p>    <br/></p>