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1. INTRODUCTION
Professor Davidson's Econometric Theory offers a clear, well written, graduate level econometrics textbook, which would be highly appropriate as the principal text for the second and/or third semester of a general Ph.D. econometrics sequence or for a specialized course on time series. It provides good reading material for anyone interested in deepening his or her understanding of econometrics and also a valuable reference source for econometricians. Although the book reviews the basics of multivariate regression, it is written at a level that requires either some degree of mathematical sophistication or prior familiarity with econometrics. As a result, it may be too advanced for a first semester Ph.D. course, except where students have strong preparation.
The text provides the reader with a deep understanding of many of the principal concepts and estimation techniques used in econometrics. It can also be seen as providing a bridge between econometric theory and practical econometric methodology. Although all the coverage is devoted to modern econometric procedures widely used in practice, the book takes "the probabilistic foundations of the subject seriously." Although, the text does not attempt to tackle the derivation of high level probability results, such as laws of large numbers and central limit theorems for heterogenous and dependent data, these results are stated and explicitly referred to when deriving the behavior of standard estimators. Along these lines, there is also a rigorous and unified treatment of the asymptotic theory for optimization estimators, which helps greatly to simplify the individual estimation techniques, such as maximum likelihood, the general method of moments, and nonlinear least squares (NLS). Similarly, treating the regressors as explicitly random from the outset and emphasizing conditional expectations helps to provide a more cohesive treatment of linear models. For example, instrumental variables and time series regression are more easily covered if students are already comfortable with random regressors. Additional themes emphasized in the text include exogeneity and model specification, partially specified models, and the pseudo-maximum likelihood interpretation of optimization estimators.
In writing the book, the author himself acknowledges an explicit preference for depth over breadth of coverage. Although the text in fact covers quite a wide range of material, no attempt is made to be all inclusive. The text is strictly classical in its approach, with...