Estimation and Inference in Econometrics by Russell Davidson

Estimation and Inference in Econometrics

byRussell Davidson, James G. MacKinnon

Hardcover | January 1, 1993

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Offering students a unifying theoretical perspective, this innovative text emphasizes nonlinear techniques of estimation, including nonlinear least squares, nonlinear instrumental variables, maximum likelihood and the generalized method of moments, but nevertheless relies heavily on simplegeometrical arguments to develop intuition. One theme of the book is the use of artificial regressions for estimation, inference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, series correlation, heteroskedasticity and other types ofmisspecification. Other topics include the linear simultaneous equations model, non-nested hypothesis tests, influential observations and leverage, transformations of the dependent variable, binary response models, models for time-series/cross-section data, multivariate models, seasonality, unitroots and cointegration, and Monte Carlo methods, always with an emphasis on problems that arise in applied work. Explaining throughout how estimates can be obtained and tests can be carried out, the text goes beyond a mere algebraic description to one that can be easily translated into thecommands of a standard econometric software package. A comprehensive and coherent guide to the most vital topics in econometrics today, this text is indispensable for all levels of students of econometrics, economics, and statistics on regression and related topics.

About The Author

Russell Davidson and James G. MacKinnon are both at Queen's University.
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Details & Specs

Title:Estimation and Inference in EconometricsFormat:HardcoverDimensions:896 pages, 6.38 × 9.57 × 1.97 inPublished:January 1, 1993Publisher:Oxford University Press

The following ISBNs are associated with this title:

ISBN - 10:0195060113

ISBN - 13:9780195060119

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Extra Content

Table of Contents

1. The Geometry of Least Squares2. Nonlinear Regression Models and Nonlinear Least Squares3. Inference in Nonlinear Regression Models4. Introduction to Asymptotic Theory and Methods5. Asymptotic Methods and Nonlinear Least Squares6. The Gauss-Newton Regression7. Instrumental Variables8. The Method of Maximum Likelihood9. Maximum Likelihood and Generalized Least Squares10. Serial Correlation11. Tests Based on the Gauss-Newton Regression12. Interpreting Tests in Regression Directions13. The Classical Hypothesis Tests14. Transforming the Dependent Variable15. Qualitative and Limited Dependent Variables16. Heteroskedasticity and Related Topics17. The Generalized Method of Moments18. Simultaneous Equations Models19. Regression Models for Time-series Data20. Unit Roots and Cointegratiaon21. Monte Carlo ExperimentsA. Matrix AlgebraB. Results from Probability TheoryReferencesAuthor IndexSubject Index

From Our Editors

Offering a unifying theoretical perspective not readily available in any other text, this innovative book uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics in econometrics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation.

Editorial Reviews

"Extremely valuable in the sense that it balances the coverage between test of hypothesis and estimation. Most books treat test of hypothesis as a side issue. The book is well-contained and easy to read. An excellent textbook."--Choon-Geol Moon, Rutgers University