
An Introduction to Econometric Theory
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In addition to covering the basic tools of empirical work in economics and finance, Gallant devotes particular attention to motivating ideas and presenting them as the solution to practical problems. For example, he presents correlation, regression, and conditional expectation as a means of obtaining the best approximation of one random variable by some function of another. He considers linear, polynomial, and unrestricted functions, and leads the reader to the notion of conditioning on a sigma-algebra as a means for finding the unrestricted solution. The reader thus gains an understanding of the relationships among linear, polynomial, and unrestricted solutions. Proofs of results are presented when the proof itself aids understanding or when the proof technique has practical value.
A major text-treatise by one of the leading scholars in this field, An Introduction to Econometric Theory will prove valuable not only to graduate students but also to all economists, statisticians, and finance professionals interested in the ideas and implications of theoretical econometrics.
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Content
- Cover
- Title Page
- Copyright Page
- CONTENTS
- Preface
- Chapter 1 - Probability
- 1.1 Examples
- 1.2 Sample Space
- 1.3 Events
- 1.4 Probability Spaces
- 1.5 Properties of Probability Spaces
- 1.6 Combinatorial Results
- 1.7 Conditional Probability
- 1.8 Independence
- 1.9 Problems
- Chapter 2 - Random Variables and Expectation
- 2.1 Random Variables
- 2.2 Continuous Random Variables
- 2.3 Discrete Random Variables
- 2.4 Unconditional Expectation
- 2.5 Conditional Expectation
- 2.6 Problems
- Chapter 3 - Distributions, Transformations, and Moments
- 3.1 Distribution Functions
- 3.2 Independent Random Variables
- 3.3 Transformations
- 3.4 Moments and Moment Generating Functions
- 3.5 Covariance and Correlation
- 3.6 The Bivariate Normal Distribution
- 3.7 Matrix Notation for Moments
- 3.8 The Multivariate Normal Distribution
- 3.9 Problems
- Chapter 4 - Convergence Concepts
- 4.1 Random Samples
- 4.2 Almost Sure Convergence
- 4.3 Convergence in Probability
- 4.4 Convergence in Distribution
- 4.5 Lp Convergence
- 4.6 Problems
- Chapter 5 - Statistical Inference
- 5.1 Inference
- 5.2 Maximum Likelihood Estimation
- 5.3 Method of Moments Estimation
- 5.4 Bayesian Estimation
- 5.5 The Wald Test
- 5.6 The Lagrange Multiplier Test
- 5.7 The Likelihood Ratio Test
- 5.8 Bayesian Hypothesis Testing
- 5.9 Problems
- Appendix: Distributions
- References
- Index
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