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This text prepares first-year graduate students and advanced undergraduates for empirical research in economics, and also equips them for specialization in econometric theory, business, and sociology.

*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 micro-data analyses, and all are ideally suited to use as homework and test questions.

### Details & Specs

The following ISBNs are associated with this title:

ISBN - 10:0674175441

ISBN - 13:9780674175440

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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 Normal Distribution

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 δ^{2} 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

Editorial Reviews