A Course in Econometrics Book

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. lndependence 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 NormalDistribution


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