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Cover

Estimation and Inference in Econometrics

Russell Davidson and James G. MacKinnon

Publication Date - January 1993

ISBN: 9780195060119

896 pages
Hardcover
6-1/8 x 9-1/4 inches

In Stock

Retail Price to Students: $99.95

Description

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 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.

Table of Contents

    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 Cointegratiaon
    21. Monte Carlo Experiments
    A. Matrix Algebra
    B. Results from Probability Theory
    References
    Author Index
    Subject Index

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