Эксоцман
на главную поиск contacts

Estimation and inference in econometrics

Опубликовано на портале: 29-09-2003
New York: Oxford University Press, 1993, 874 с.
Тематический раздел:
Davidson and MacKinnon have written an outstanding textbook for graduates in econometrics, covering both basic and advanced topics and using geometrical proofs throughout for clarity of exposition. The book offers a unified theoretical perspective, and emphasizes the practical applications of modern theory.

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 simple geometrical 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 of misspecification. 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, unit roots 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 the commands 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.

На сайте интернет-магазина Amazon.com можно прочитать первые сорок одну страницу этой книги в pdf-формате, рецензии, а также приобрести ее.

  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
    A. Matrix Algebra
    B. Results from Probability Theory

    References

    Author Index

    Subject Index