Estimation and inference in econometrics
Опубликовано на портале: 29-09-2003
New York: Oxford University Press, 1993, 874 с.
Тематический раздел:
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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. |
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- The Geometry of Least Squares
- Nonlinear Regression Models and Nonlinear Least Squares
- Inference in Nonlinear Regression Models
- Introduction to Asymptotic Theory and Methods
- Asymptotic Methods and Nonlinear Least Squares
- The Gauss-Newton Regression
- Instrumental Variables
- The Method of Maximum Likelihood
- Maximum Likelihood and Generalized Least Squares
- Serial Correlation
- Tests Based on the Gauss-Newton Regression
- Interpreting Tests in Regression Directions
- The Classical Hypothesis Tests
- Transforming the Dependent Variable
- Qualitative and Limited Dependent Variables
- Heteroskedasticity and Related Topics
- The Generalized Method of Moments
- Simultaneous Equations Models
- Regression Models for Time-Series Data
- Unit Roots and Cointegration
- Monte Carlo Experiments
A. Matrix Algebra
B. Results from Probability TheoryReferences
Author Index
Subject Index
Ключевые слова
asymptotic methods asymptotic theory cointegration generalized least squares generalized method of moments heteroskedasticity limited dependent variable maximum likelihood monte carlo method nonlinear least squares nonlinear regression models qualitative variable serial correlation simultaneous equations model unit root
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