- 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
Introductory Econometrics
Product Details
HARDCOVER
$99.00 • £86.95 • €90.95
ISBN 9780674461079
Publication Date: 09/20/1998
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