Cover: A Course in Econometrics in HARDCOVER

A Course in Econometrics

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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

  • 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. Advanced Estimation Theory
    • 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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