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Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that
can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.
Readership: First- and second-year graduate
students taking courses in econometrics. Professional econometricians wishing to update their skills.
Russell Davidson, Professor of Economics, Queen's University, Canada, and Université d'Aix-Marseille II & III, France, and James G. MacKinnon, Professor of Economics, Queen's University, Canada
"`Excellent reference text on econometrics.'
Ron Smith, Birkbeck College, London"
"`This is a very good overall post-graduate textbook in econometrics.'
Dr Offer Lieberman, University of Bristol"
"`a welcome addition to the long bookshelf of choices available to those teaching graduate econometrics ... the book is remarkably self-contained and internally integrated ... the Davidson and Mackinnon volume represents an excellent choice from a range of fine texts. I look forward to using it in my classes as an extension of the Gujarati text'
1: The Geometry of Least Squares
2: Nonlinear Regression Models and Nonlinear Least Squares
3: Inference in Nonlinear Regression Models
4: Introduction to Asymptotic Theory and Methods
5: Asymptotic Methods and Nonlinear Least Squares
6: The Gauss-Newton Regression
7: Instrumental Variables
8: The Method of Maximum Likelihood
9: Maximum Likelihood and Generalized Least Squares
10: Serial Correlation
11: Tests Based on the Gauss-Newton Regression
12: Interpreting Tests in Regression Directions
13: The Classical Hypothesis Tests
14: Transforming the Dependent Variable
15: Qualitative and Limited Dependent Variables
16: Heteroskedasticity and Related Topics
17: The Generalized Method of Moments
18: Simultaneous Equations Models
19: Regression Models for Time-Series Data
20: Unit Roots and Cointegration
21: Monte Carlo Experiments