Acknowledgments

Preface

About the Author

Chapter 1. The Nature and Scope of Econometrics

1.1 What Is Econometrics?

1.2 Why Study Econometrics?

1.3 The Methodology Of Econometrics

Appendix 1A: Economic Data on the World Wide Web

PART I. THE LINEAR REGRESSION MODEL

Chapter 2. Basic Ideas of Linear Regression: The Two-Variable Model

2.1 The Meaning of Regression

2.2 The Population Regression Function (PRF): A Hypothetical Example

2.3 Statistical or Stochastic Specification of The Population Regression Function

2.4 The Nature of the Stochastic Error Term

2.5 The Sample Regression Function (SRF)

2.6 The Special Meaning of the Term Linear Regression

2.7 Two-Variable Versus Multiple Linear Regression

2.8 Estimation of Parameters: The Method of Ordinary Least Squares

2.9 Putting It All Together

2.10 Some Illustrative Examples

Appendix 2A: Derivation of Least Squares Estimators

Chapter 3. The Two-Variable Model: Hypothesis Testing

3.1 The Classical Linear Regression Model

3.2 Variances and Standard Errors of Ordinary Least Squares Estimators

3.3 Why OLS? Properties of OLS Estimators

3.4 The Sampling, or Probability, Distributions of OLS Estimators

3.6 Hypothesis Testing: Some Practical Aspects

3.7 How Good Is The Fitted Regression Line: The Coefficient of Determination, r2

3.8 Reporting the Results of Regression Analysis

3.9 Illustrative Examples

3.10 Comments on the Illustrative Examples

Chapter 4. Multiple Regression: Estimation and Hypothesis Testing

4.1 The Three-Variable Linear Regression Model

4.2 Assumptions of the Multiple Linear Regression Model

4.3 Estimation of the Parameters of Multiple Regression

4.4 Goodness of Fit of Estimated Multiple Regression: Multiple Coefficient of Determination, R2

4.5 Antique Clock Auction Prices Revisited

4.6 Hypothesis Testing In A Multiple Regression: General Comments

4.7 Testing Hypotheses About Individual Partial Regression Coefficients

4.8 Testing the Joint Hypothesis That B2 = B3 = 0 Or R2 = 0

4.9 Two-Variable Regression In the Context of Multiple Regression: Introduction to Specification Bias

4.10 Comparing Two R2 Values: The Adjusted R2

4.11 When to Add an Additional Explanatory Variable to a Model

4.12 Restricted Least Squares

4.13 Illustrative Examples

Appendix 4A.1: Derivations of OLS Estimators

Appendix 4A.2: Derivation of Equation (4.31)

Appendix 4A.3: Derivation of Equation (4.49)

Chapter 5. Functional Forms of Regression Models

5.1 How to Measure Elasticity: The Log-Linear Model

5.2 Multiple Log-Linear Regression Models

5.3 How to Measure the Growth Rate: The Semilog Model

5.4 The Lin-Log Model: When the Explanatory Variable Is Logarithmic

5.6 Polynomial Regression Models

5.7 Regression Through the Origin: The Zero Intercept Model

5.8 A Note on Scaling and Units of Measurement

5.9 Regression on Standardized Variables

5.10 Summary of Functional Forms

Chapter 6. Qualitative or Dummy Variable Regression Models

6.1 The Nature of Dummy Variables

6.2 ANCOVA Models: Regression on One Quantitative Variable and One Qualitative Variable With Two Categories

6.3 Regression on One Quantitative Variable and One Qualitative Variable With More Than Two Classes or Categories

6.4 Regression on One Quantiative Explanatory Variable and More Than One Qualitative Variable

6.5 Comparing Two Regessions

6.6 The Use of Dummy Variables In Seasonal Analysis

6.7 What Happens if the Dependent Variable Is Also a Dummy Variable? The Linear Probability Model (LPM)

PART II. REGRESSION ANALYSIS IN PRACTICE

Chapter 7. Model Selection: Criteria and Tests

7.1 The Attributes of a Good Model

7.2 Types of Specification Errors

7.3 Omisson of Relevant Variable Bias: “Underfitting” a Model

7.4 Inclusion of Irrelevant Variables: “Overfitting” a Model

7.5 Incorrect Functional Form

7.6 Errors of Measurement

7.7 Detecting Specification Errors: Tests of Specification Errors

7.8 Outliers, Leverage, and Influence Data

7.9 Probabity Distribution of the Error Term

7.10 Model Evaluation Criteria

7.11 Nonnormal Distribution of the Error Term

7.12 Fixed Versus Random (or Stochastic) Explanatory Variables

Chapter 8. Multicollinearity: What Happens if Explanatory Variables Are Correlated?

8.1 The Nature of Multicollinearity: The Case of Perfect Multicollinearity

8.2 The Case of Near, or Imperfect, Multicollinearity

8.3 Theoretical Consequences of Multicollinearity

8.4 Practical Consequences of Multicollinearity

8.5 Detection of Multicollinearity

8.6 Is Multicollinearity Necessarily Bad?

8.7 An Extended Example: The Demand for Chickens In The United States, 1960 To 1982

8.8 What to Do With Multicollinearity: Remedial Measures

Chapter 9. Heteroscedasticity: What Happens if the Error Variance Is Nonconstant?

9.1 The Nature of Heteroscedasticity

9.2 Consequences of Heteroscedasticity

9.3 Detection of Heteroscedasticity: How Do We Know When There Is a Heteroscedasticity Problem?

9.4 What to Do if Heteroscedasticity Is Observed: Remedial Measures

9.5 White’s Heteroscedasticity-Corrected Standard Errors and t Statistics

9.6 Some Concrete Examples of Heteroscedasticity

Chapter 10. Autocorrelation: What Happens If Error Terms Are Correlated?

10.1 The Nature of Autocorrelation

10.2 Consequences of Autocorrelation

10.3 Detecting Autocorrelation

10.6 A Large Sample Method of Correcting OLS Standard Errors: The Newey–West (NW) Method

10.7 A General Test of Autocorrelation: The Breusch–Godfrey (BG) Test

PART III. ADVANCED TOPICS IN ECONOMETRICS

Chapter 11. Elements of Time-Series Econometrics

11.1 The Phenomenon of Spurious Regression: Nonstationary Time Series

11.2 Tests of Stationarity

11.3 Cointegrated Time Series

11.4 The Random Walk Model

11.5 Causality In Economics: The Granger Causality Test

Chapter 12. Panel Data Regression Models

12.1 The Importance of Panel Data

12.2 An Illustrative Example: Charitable Giving

12.3 Pooled OLS Regression of the Charity Function

12.4 The Fixed-Effects Least Squares Dummy Variable (LSDV) Model

12.5 Limitations of the Fixed-Effects LSDV Model

12.6 The Fixed-Effects Within-Group (WG) Estimator

12.7 The Random-Effects Model (REM) or Error Components Model (ECM)

12.8 Properties of Various Estimators

12.9 Panel Data Regressions: Some Concluding Comments

12.10 Summary and Conclusions

INTRODUCTION TO APPENDIXES A, B, C, AND D: BASICS OF PROBABILITY AND STATISTICS

Appendix A: Review of Statistics: Probability and Probability Distributions

A.2 Experiment, Sample Space, Sample Point, and Events

A.5 Random Variables and Their Probability Distributions

A.6 Multivariate Probability Density Functions

A.7 Summary and Conclusions

Appendix B: Characteristics of Probability Distributions

B.1 Expected Value: A Measure of Central Tendency

B.2 Variance: A Measure of Dispersion

B.4 Correlation Coefficient

B.5 Conditional Expectation

B.6 Skewness and Kurtosis

B.7 From Population to the Sample

Appendix C: Some Important Probability Distributions

C.1 The Normal Distribution

C.3 The Chi-Square (?2) Probability Distribution

Appendix D: Statistical Inference: Estimation and Hypothesis Testing

D.1 The Meaning of Statistical Inference

D.2 Estimation and Hypothesis Testing: Twin Branches of Statistical Inference

D.3 Estimation of Parameters

D.4 Properties of Point Estimators

D.5 Statistical Inference: Hypothesis Testing

Appendix E: Statistical Tables

Index