# A Course in Econometrics

### Arthur S. Goldberger

#### Product Details

HARDCOVER

\$100.00 • £80.95 • €90.00

ISBN 9780674175440

Publication Date: 04/15/1991

Short

432 pages

6-1/8 x 9-1/4 inches

32 line illustrations, 9 tables

World

#### Related Subjects

• 1. Empirical Relations
• 1.1 Theoretical and Empirical Relations
• 1.2 Sample Means and Population Means
• 1.3 Sampling
• 1.4 Estimation
• Exercises
• 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
• Exercises
• 3. Expectations: Univariate Case
• 3.1 Expectations
• 3.2 Moments
• 3.3 Theorems on Expectations
• 3.4 Prediction
• 3.5 Expectations and Probabilities
• Exercises
• 4. Bivariate Probability Distributions
• 4.1 Joint Distributions
• 4.2 Marginal Distributions
• 4.3 Conditional Distributions
• Exercises
• 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
• Exercises
• 6. Independence 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 12.1 The Score Variable
• 12.2 Cramér-Rao Inequality
• 12.3 ZES-Rule Estimation
• 12.4 Maximum Likelihood Estimation
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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)
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 23. Multicollinearity
• 23.1 Introduction
• 23.2 Textbook Discussions
• 23.3 Micronumerosity
• 23.4 When Multicollinearity Is Desirable
• 23.5 Remarks
• Exercises
• 24. Regression Strategies
• 24.1 Introduction
• 24.2 Shortening a Regression
• 24.3 Mean Squared Error
• 24.4 Pretest Estimation
• 24.5 Regression Fishing
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 28. Heteroskedasticity and Autocorrelation
• 28.1 Introduction
• 28.2 Pure Heteroskedasticity
• 28.3 First-Order Autoregressive Process
• 28.4 Remarks
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• 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
• Exercises
• Appendix A. Statistical and Data Tables
• Appendix B. Getting Started in GAUSS
• References
• Index

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