A Course in Econometrics by Arthur S. GoldbergerA Course in Econometrics by Arthur S. Goldberger

A Course in Econometrics

byArthur S. Goldberger

Hardcover | April 15, 1991

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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.

Arthur S. Goldberger is Professor of Economics, Emeritus at the University of Wisconsin-Madison.
Title:A Course in EconometricsFormat:HardcoverDimensions:432 pages, 9.25 × 6.13 × 1 inPublished:April 15, 1991Publisher:Harvard

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


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


3. Expectations: Univariate Case

3.1 Expectations

3.2 Moments

3.3 Theorems on Expectations

3.4 Prediction

3.5 Expectations and Probabilities


4. Bivariate Probability Distributions

4.1 Joint Distributions

4.2 Marginal Distributions

4.3 Conditional Distributions


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


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


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


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


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


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


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


12. Advanced Estimation Theory

12.1 The Score Variable

12.2 Cramér-Rao Inequality

12.3 ZES-Rule Estimation

12.4 Maximum Likelihood Estimation


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


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


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)


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


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


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


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


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


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


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


23. Multicollinearity

23.1 Introduction

23.2 Textbook Discussions

23.3 Micronumerosity

23.4 When Multicollinearity Is Desirable

23.5 Remarks


24. Regression Strategies

24.1 Introduction

24.2 Shortening a Regression

24.3 Mean Squared Error

24.4 Pretest Estimation

24.5 Regression Fishing


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


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


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


28. Heteroskedasticity and Autocorrelation

28.1 Introduction

28.2 Pure Heteroskedasticity

28.3 First-Order Autoregressive Process

28.4 Remarks


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


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


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


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


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


Appendix A. Statistical and Data Tables

Appendix B. Getting Started in GAUSS



Editorial Reviews

This book is an excellent choice for first year graduate econometrics courses because it provides a solid foundation in statistical reasoning in a manner that is both clear and concise. It addresses a number of issues that are of central importance to developing practitioners and theorists alike and achieves this in a fairly nontechnical manner...The topics addressed here are rarely given such a thorough treatment in econometrics textbooks. For example, in discussions of bivariate distributions, Goldberger points out that two uncorrelated normal random variables may not be independent, since a nonnormal bivariate distribution can generate normal marginal distributions. Other texts typically leave readers with the impression that two uncorrelated normal random variables are independent without reference to their joint distribution...A Course in Econometrics is rigorous, it makes students think hard about important issues, and it avoids a cookbook approach. For these reasons, I strongly recommend it as a basic text for all first year graduate econometrics courses.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.