Intended primarily to prepare first-year graduate students for their ongoing work in econometrics, economic theory, and finance, this innovative book presents the fundamental concepts of theoretical econometrics, from measure-theoretic probability to statistics. A. Ronald Gallant covers these topics at an introductory level and develops the ideas to the point where they can be applied. He thereby provides the reader not only with a basic grasp of the key empirical tools but with sound intuition as well.

In addition to covering the basic tools of empirical work in economics and finance, Gallant devotes particular attention to motivating ideas and presenting them as the solution to practical problems. For example, he presents correlation, regression, and conditional expectation as a means of obtaining the best approximation of one random variable by some function of another. He considers linear, polynomial, and unrestricted functions, and leads the reader to the notion of conditioning on a sigma-algebra as a means for finding the unrestricted solution. The reader thus gains an understanding of the relationships among linear, polynomial, and unrestricted solutions. Proofs of results are presented when the proof itself aids understanding or when the proof technique has practical value.

A major text-treatise by one of the leading scholars in this field, An Introduction to Econometric Theory will prove valuable not only to graduate students but also to all economists, statisticians, and finance professionals interested in the ideas and implications of theoretical econometrics.

Review:

"This is an excellent book . . . There are chapters on probability, random variables and expectations, distributions and convergence concepts. . . . It is very concise, yet treat most relevant topics in a clear and precise way."--Mathematical Reviews

Endorsement:

"An excellent book. It covers the measure-theoretic material in a very understandable way, while offering some very neat proofs and motivating arguments. Professionals as well as students will want to buy this text, as it offers a very useful compendium of results that one can refer to."--Adrian Pagan, Australian National University in Canberra

Ron Gallant is Distinguished Scientist in Residence, Department of Economics, New York University and Hanes Corporation Foundation Professor of Business Administration, Fuqua School of Business, Duke University, with secondary appointment in the Department of Economics, Duke University. Before joining the Duke faculty, he was Henry A. Latane Distinguished Professor of Economics at the University of North Carolina at Chapel Hill. He retains emeritus status at UNC. Previously he was, successively, Assistant, Associate, Full, and Drexel Professor of Statistics and Economics at North Carolina State University. Gallant has held visiting positions at the University of Chicago, Duke University, and Northwestern University. He received his A.B. in mathematics from San Diego State University, his M.B.A. in marketing from the University of California at Los Angeles, and his Ph.D. in statistics from Iowa State University. He is a Fellow of both the Econometrics Society and the American Statistical Association. He has served on the Board of Directors of the National Bureau of Economic Research, the Board of Directors of the American Statistical Association, and on the Board of Trustees of the National Institute of Statistical Sciences. He is co-editor of the Journal of Econometrics and past editor of The Journal of Business and Economic Statistics.

Gallant is interested in fitting models from the sciences to data for the purpose of statistical inference. Typically these models will involve a nonlinear parametric component that describes features of the model where the underlying scientific theory is explicit and a nonparametric component that accounts for features where the scientific theory is vague. Appropriate statistical methods for these problems are usually computationally intensive. Methodological interests are in developing statistical methods and numerical algorithms for fitting these models. Theoretical interests are in deriving the statistical properties of proposed methods, particularly the asymptotic properties of estimators of functionals of the nonparametric component. Applied interests are primarily within economics and finance.