A Course in Econometrics / Edition 1 available in Hardcover
A Course in Econometrics / Edition 1
- ISBN-10:
- 0674175441
- ISBN-13:
- 9780674175440
- Pub. Date:
- 04/15/1991
- Publisher:
- Harvard University Press
- ISBN-10:
- 0674175441
- ISBN-13:
- 9780674175440
- Pub. Date:
- 04/15/1991
- Publisher:
- Harvard University Press
A Course in Econometrics / Edition 1
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Overview
A Course in Econometrics is likely to be the text most thoroughly attuned to the needs of your students. Derived from the course taught by Arthur S. Goldberger at the University of Wisconsin–Madison and at Stanford University, it is specifically designed for use over two semesters, offers students the most thorough grounding in introductory statistical inference, and offers a substantial amount of interpretive material. The text brims with insights, strikes a balance between rigor and intuition, and provokes students to form their own critical opinions.
A Course in Econometrics thoroughly covers the fundamentals—classical regression and simultaneous equations—and offers clear and logical explorations of asymptotic theory and nonlinear regression. To accommodate students with various levels of preparation, the text opens with a thorough review of statistical concepts and methods, then proceeds to the regression model and its variants. Bold subheadings introduce and highlight key concepts throughout each chapter.
Each chapter concludes with a set of exercises specifically designed to reinforce and extend the material covered. Many of the exercises include real microdata analyses, and all are ideally suited to use as homework and test questions.
Product Details
| ISBN-13: | 9780674175440 |
|---|---|
| Publisher: | Harvard University Press |
| Publication date: | 04/15/1991 |
| Edition description: | New Edition |
| Pages: | 432 |
| Product dimensions: | 6.12(w) x 9.25(h) x 1.20(d) |
About the Author
Table of Contents
1. Empirical Relations
1.1 Theoretical and Empirical Relations
1.2 Sample Means and Population Means
1.3 Sampling
1.4 Estimation
Exercises
2. Univariate Probability Distributions
2.1 Introduction
2.2 Discrete Case
2.3 Continuous Case
2.4 Mixed Case
2.5 Functions of Random Variables
Exercises
3. Expectations: Univariate Case
3.1 Expectations
3.2 Moments
3.3 Theorems on Expectations
3.4 Prediction
3.5 Expectations and Probabilities
Exercises
4. Bivariate Probability Distributions
4.1 Joint Distributions
4.2 Marginal Distributions
4.3 Conditional Distributions
Exercises
5. Expectations Bivariate Case
5.1 Expectations
5.2 Conditional Expectations
5.3 Conditional Expectation Function
5.4 Prediction
5.5 Conditional Expectations and Linear Predictors
Exercises
6. lndependence in a Bivariate Distribution
6.1 Introduction
6.2 Stochastic Independence
6.3 Roles of Stochastic Independence
6.4 Mean-Independence and Uncorrelatedness
6.5 Types of Independence
6.6 Strength of a Relation
Exercises
7. Normal Distributions
7.1 Univariate Normal Distribution
7.2 Standard Bivariate Normal Distribution
7.3 Bivariate Normal Distribution
7.4 Properties of Bivariate NormalDistribution
7.5 Remarks
Exercises
8. Sampling Distributions Univariate Case
8.1 Random Sample
8.2 Sample Statistics
8.3 The Sample Mean
8.4 Sample Moments
8.5 Chi-square and Student's Distributions
8.6 Sampling from a Normal Population
Exercises
9. Asymptotic Distribution Theory
9.1 Introduction
9.2 Sequences of Sample Statistics
9.3 Asymptotics of the Sample Mean
9.4 Asymptotics of Sample Moments
9.5 Asymptotics of Functions of Sample Moments
9.6 Asymptotics of Some Sample Statistics
Exercises
10. Sampling Distributions Bivariate Case
10.1 Introduction
10.2 Sample Covariance
10.3 Pair of Sample Means
10.4 Ratio of Sample Means
10.5 Sample Slope
10.6 Variance of Sample Slope
Exercises
11. Parameter Estimation
11.1 Introduction
11.2 The Analogy Principle
11.3 Criteria for an Estimator
11.4 Asymptotic Criteria
11.5 Confidence Intervals
Exercises
12. Advanced Estimation Theory
12.1 The Score Variable
12.2 Cramér-Rao Inequality
12.3 ZES-Rule Estimation
12.4 Maximum Likelihood Estimation
Exercises
13. Estimating a Population Relation
13.1 Introduction
13.2 Estimating a Linear CEF
13.3 Estimating a Nonlinear CEF
13.4 Estimating a Binary Response Model
13.5 Other Sampling Schemes
Exercises
14. Multiple Regression
14.1 Population Regression Function
14.2 Algebra for Multiple Regression
14.3 Ranks of X and Q
14.4 The Short-Rank Case
14.5 Second-Order Conditions
Exercises
15. Classical Regression
15.1 Matrix Algebra for Random Variables
15.2 Classical Regression Model
15.3 Estimation of β165
15.4 Gauss-Markov Theorem
15.5 Estimation of δ2 and V(b)
Exercises
16. Classical Regression Interpretation and Application
16.1 Interpretation of the Classical Regression Model
16.2 Estimation of Linear Functions of β13
16.3 Estimation of Conditional Expectation, and Prediction
16.4 Measuring Goodness of Fit
Exercises
17. Regression Algebra
17.1 Regression Matrices
17.2 Short and Long Regression Algebra
17.3 Residual Regression
17.4 Applications of Residual Regression
17.5 Short and Residual Regressions in the Classical Regression Model
Exercises
18. Multivariate Normal Distribution
18.1 Introduction
18.2 Multivariate Normality
18.3 Functions of a Standard Normal Vector
18.4 Quadratic Forms in Normal Vectors
Exercises
19. Classical Normal Regression
19.1 Classical Normal Regression Model
19.2 Maximum Likelihood Estimation
19.3 Sampling Distributions
19.4 Confidence Intervals
19.5 Confidence Regions
19.6 Shape of the Joint Confidence Region
Exercises
20. CNR Model Hypothesis Testing
20.1 Introduction
20.2 Test on a Single Parameter
20.3 Test on a Set of Parameters
20.4 Power of the Test
20.5 Noncentral Chi-square Distribution
Exercises
21. CNR Model Inference with
Unknown
21.1 Distribution Theory
21.2 Confidence Intervals and Regions
21.3 Hypothesis Tests
21.4 Zero Null Subvector Hypothesis
Exercises
22. Issues in Hypothesis Testing
22.1 Introduction
22.2 General Linear Hypothesis
22.3 One-Sided Alternatives
22.4 Choice of Significance Level
22.5 Statistical versus Economic Significance
22.6 Using Asymptotics
22.7 Inference without Normality Assumption
Exercises
23. Multicollinearity
23.1 Introduction
23.2 Textbook Discussions
23.3 Micronumerosity
23.4 When Multicollinearity Is Desirable
23.5 Remarks
Exercises
24. Regression Strategies
24.1 Introduction
24.2 Shortening a Regression
24.3 Mean Squared Error
24.4 Pretest Estimation
24.5 Regression Fishing
Exercises
25. Regression with X Random
25.1 Introduction
25.2 Neoclassical Regression Model
25.3 Properties of Least Squares Estimation
25.4 Neoclassical Normal Regression Model
25.5 Asymptotic Properties of Least Squares Estimation
Exercises
26. Time Series
26.1 Departures from Random Sampling
26.2 Stationary Population Model
26.3 Conditional Expectation Functions
26.4 Stationary Processes
26.5 Sampling and Estimation
26.6 Remarks
Exercises
27. Generalized Classical Regression
27.1 Generalized Classical Regression Model
27.2 Least Square Estimation
27.3 Generalized Least Square Estimation
27.4 Remarks on GL Estimation
27.5 Feasible Generalized Least Squares Estimation
27.6 Extensions of the GCR Model
Exercises
28. Heteroskedasticity and Autocorrelation
28.1 Introduction
28.2 Pure Heteroskedasticity
28.3 First-Order Autoregressive Process
28.4 Remarks
Exercises
29. Nonlinear Regression
29.1 Nonlinear CEF's
29.2 Estimation
29.3 Computation of the Nonlinear Least Squares Estimator
29.4 Asymptotic Properties
29.5 Probit Model
Exercises
30. Regression Systems
30.1 Introduction
30.2 Stacking
30.3 Generalized Least Squares
30.4 Comparison of GLS and LS Estimators
30.5 Feasible Generalized Least Squares
30.6 Restrictions
30.7 Alternative Estimators
Exercises
31. Structural Equation Models
31.1 Introduction
31.2 Permanent Income Model
31.3 Keynesian Model
31.4 Estimation of the Keynesian Model
31.5 Structure versus Regression
Exercises
32. Simultaneous-Equation Model
32.1 A Supply-Demand Model
32.2 Specification of the Simultaneous-Equation Model
32.3 Sampling
32.4 Remarks
33. Identification and Restrictions
33.1 Introduction
33.2 Supply-Demand Models
33.3 Uncorrelated Disturbances
33.4 Other Sources of Identification
Exercises
34. Estimation in the Simultaneous-Equation Model
34.1 Introduction
34.2 Indirect Feasible Generalized Least Squares
34.3 Two-Stage Least Squares
34.4 Relation between 2SLS and Indirect-FGLS
34.5 Three-Stage Least Squares
34.6 Remarks
Exercises
Appendix A. Statistical and Data Tables
Appendix B. Getting Started in GAUSS
References
Index
What People are Saying About This
Undoubtedly the best Ph.D. level econometrics textbook available today. The analogy principle of estimation serves to unify the treatment of a wide range of topics that are at the foundation of empirical economics. The notation is concise and consistently used throughout the text...Students have expressed delight in unraveling the proofs and lemmas. It's a pleasure to teach from this book. Recommended for any serious economics student or anyone interested in studying the principles underlying applied economics.
Undoubtedly the best Ph.D. level econometrics textbook available today. The analogy principle of estimation serves to unify the treatment of a wide range of topics that are at the foundation of empirical economics. The notation is concise and consistently used throughout the text...Students have expressed delight in unraveling the proofs and lemmas. It's a pleasure to teach from this book. Recommended for any serious economics student or anyone interested in studying the principles underlying applied economics.
— Michael Hazilla, American University