Introductory Econometrics by Arthur S. GoldbergerIntroductory Econometrics by Arthur S. Goldberger

Introductory Econometrics

byArthur S. Goldberger

Hardcover

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This is a textbook for the standard undergraduate econometrics course. Its only prerequisites are a semester course in statistics and one in differential calculus. Arthur Goldberger, an outstanding researcher and teacher of econometrics, views the subject as a tool of empirical inquiry rather than as a collection of arcane procedures. The central issue in such inquiry is how one variable is related to one or more others. Goldberger takes this to mean "How does the average value of one variable vary with one or more others?" and so takes the population conditional mean function as the target of empirical research.

The structure of the book is similar to that of Goldberger's graduate-level textbook, A Course in Econometrics, but the new book is richer in empirical material, makes no use of matrix algebra, and is primarily discursive in style. A great strength is that it is both intuitive and formal, with ideas and methods building on one another until the text presents fairly complicated ideas and proofs that are often avoided in undergraduate econometrics.

To help students master the tools of econometrics, Goldberger provides many theoretical and empirical exercises and, on an accompanying diskette, real micro-and macroeconomic data sets. The data sets deal with earnings and education, money demand, firm investment, stock prices, compensation and productivity, and the Phillips curve.

THE DATA SETS CAN BE FOUND HERE.

Arthur S. Goldberger is Professor of Economics, Emeritus at the University of Wisconsin-Madison.
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Title:Introductory EconometricsFormat:HardcoverDimensions:256 pagesPublisher:HarvardLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:067446107X

ISBN - 13:9780674461079

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Table of Contents

Preface

1. Empirical Relations

Introduction
Data Sets
Other Resources
Exercises

2. Fitting the Data

The Data
Least-Squares Fitting
Useful Algebra
Other Least-Squares Problem
Exercises

3. Univariate Populations

Probability Distributions
Expected Values
Linear Function Rules
Prediction Problem
Continuous Probability Distributions
Normal Distributions
Exercises

4. Bivariate Populations

Bivariate Probability Distributions
Derived Distributions
Additional Linear Function Rules
Prediction
Other Features
Exercises

5. Inference about a Population Mean

Sampling Distributions
Sample Mean Theorem
Estimation
Asymptotic Distributions
Sample Variance
Further Inference
Practical Inference
Exercises

6. Classical Regression Model

Introduction
Sampling
Classical Regression Model
Estimation
Violations
Exercises

7. Inference in the Classical Model

Introduction
Standard Errors
Practical Inference
Hypothesis Testing
Functional Form
Exercises

8. Prediction and Fit

Prediction
Coefficient of Determination
Using R2
Prediction Revisited
Exercises

9. Multiple Regression: Preliminaries

Introduction
Fitting the Data
Interpretation
Coefficient of Determination
Trivariate Population
Exercises

10. Multiple Regression: Classical Model

Model
Estimation
Inference
Short versus Long Regression
Exercises

11. Multiple Regression: Applications

Introduction
Short versus Long Regression
Zero-Slope Null Hypothesis
Allocating R2
Relative Importance
Both Slopes Zero Null Hypothesis
Paradox?
Exercises

12. Multiple Regression: General Case

Fitting the Data
Model
Estimation
Functional Form
Hypothesis Testing
Other Linear Hypotheses
Exercises

13. Relaxing the Assumptions of the Classical Model

Background
Quadratic Regression
Heteroskedasticity
Autocorrelation
Random Sampling
Arbitrary Population
Exercises

14. Heteroskedasticity

Introduction
Model
Least Squares
Weighted Least Squares
Knowledge of Variances
Practical Considerations
Exercises

15. Autocorrelation: Preliminaries

Introduction
Model
Least-Squares Regression
Autocorrelated Data
Sample Autoregressions
Stochastic Processes
Caution
Exercises

16. Regression with Autocorrelation

Introduction
Special Cases
Correcting Standard Errors
Generalized Difference Method
Practical Considerations
Testing against Autocorrelation
Caution
Lagged Dependent Variable
Exercises

17. Binary Response Models

Binary Dependent Variable
Probability Distributions
Binary Response Model
Logistic Model
Probit Model
Interpretation
Goodness of Fit
Exercises
Appendix: Maximum-Likelihood Principle

18. Simultaneity: Preliminaries

Simultaneous-Equation Models
A Supply-Demand Model
A Keynesian Model
Estimation
Interpretation
Exercises

19. Models of Demand and Supply

Introduction
Structural Form
Reduced Form
Identification
Identification Revisited
Variants of the Model
Order Condition
Caution
Exercises

20. Estimation of Simultaneous-Equation Models

Introduction
Indirect Least Squares
Two-Stage Least Squares
Caution
Empire Example
Rationale for Two-Stage Least Squares
Exercises

Appendix: Statistical Tables

References

Index

Editorial Reviews

Introductory Econometrics carefully and clearly presents the essential materials of the classical linear regression model without the confusing and unnecessary discussions that fill many of the existing undergraduate econometric textbooks. The text is precise, intuitive, and well-written, with the key ideas and methods building on each other throughout. The book should be accessible and challenging to a wide range of undergraduates.