An Introduction to Classical Econometric Theory 1st edition by Paul Ruud – Ebook PDF Instant Download/Delivery: 0195111644 , 9780195111644
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Product details:
ISBN 10: 0195111644
ISBN 13: 9780195111644
Author: Paul Ruud
In An Introduction to Classical Econometric Theory Paul A. Ruud shows the practical value of an intuitive approach to econometrics. Students learn not only why but how things work. Through geometry, seemingly distinct ideas are presented as the result of one common principle, making econometrics more than mere recipes or special tricks. In doing this, the author relies on such concepts as the linear vector space, orthogonality, and distance. Parts I and II introduce the ordinary least squares fitting method and the classical linear regression model, separately rather than simultaneously as in other texts. Part III contains generalizations of the classical linear regression model and Part IV develops the latent variable models that distinguish econometrics from statistics. To motivate formal results in a chapter, the author begins with substantive empirical examples. Main results are followed by illustrative special cases; technical proofs appear toward the end of each chapter. Intended for a graduate audience, An Introduction to Classical Econometric Theory fills the gap between introductory and more advanced texts. It is the most conceptually complete text for graduate econometrics courses and will play a vital role in graduate instruction.
An Introduction to Classical Econometric Theory 1st Table of contents:
Introduction
Sets the stage for the content that follows.
I. Ordinary Least Squares (OLS)
Focuses on foundational regression theory:
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The Least Squares Linear Fit
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The Geometry of Least Squares
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Partitioned Fit
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Restricted Least Squares
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Overview of Ordinary Least Squares
II. Linear Regression
Expands on estimation and inference in the classical linear model:
6. Linear Unbiased Estimation
7. Variances and Covariances
8. Variances and Covariances of OLS
9. Efficient Estimation
10. Normal Distribution Theory
11. Hypothesis Testing
12. Overview of Linear Regression
III. Generalizations of the Linear Model
Covers more advanced and robust estimation techniques:
13. Non-Normal Distribution Theory
14. Maximum Likelihood Estimation
15. ML Asymptotics
16. ML Computation
17. ML Statistical Inference
18. Heteroskedasticity
19. Serial Correlation
20. IV Estimation (Instrumental Variables)
21. The Generalized Method of Moments (GMM)
22. GMM Hypothesis Tests
23. Overview
IV. Latent Variable Models
Deals with models involving unobserved or hidden variables:
24. Panel Data Models
25. ARMA Time Series Models
26. Simultaneous Equations
27. Discrete Dependent Variables
28. Censored and Truncated Variables
29. Overview
V. Appendices
Useful references for technical tools and foundational theory:
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A. Abbreviations and Acronyms
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B. Notation
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C. Linear Algebra and Matrix Theory
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D. Probability
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E. Classical Studies
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F. Noncentral Distributions
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G. Multivariate Differentiation
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H. Characteristic Functions
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Tags: Paul Ruud, An Introduction, Classical Econometric