**You are here:**

### Pricing and Purchase Info

$118.50

^{®}points

Prices and offers may vary in store

### about

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.

### Details & Specs

The following ISBNs are associated with this title:

ISBN - 10:067446107X

ISBN - 13:9780674461079

Look for similar items by category:

### Customer Reviews of Introductory Econometrics

### Extra Content

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 R^{2}

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 R^{2}

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