A Guide to Econometrics, 6ed

Peter Kennedy

ISBN: 9788126574810

600 pages

Exclusively distributed by Shri Adhya Educational Books

 

Description

This is the perfect (and essential) supplement for all econometrics classes--from a rigorous first undergraduate course, to a first master's, to a PhD course. It explains what is going on in textbooks full of proofs and formulas. Kennedy's A Guide to Econometrics offers intuition, skepticism, insights, humor, and practical advice (dos and don'ts). The sixth edition contains new chapters on instrumental variables and on computation considerations, more information on GMM and nonparametrics, and an introduction to wavelets.

Preface.

Dedication.

 

1. Introduction.

1.1 What is Econometrics?.

1.2 The Disturbance Term.

1.3 Estimates and Estimators.

1.4 Good and Preferred Estimators.

 

2. Criteria for Estimators.

2.1 Introduction.

2.2 Computational Cost.

2.3 Least Squares.

2.4 Highest R2.

2.5 Unbiasedness.

2.6 Efficiency.

2.7 Mean Square Error (MSE).

2.8 Asymptotic Properties.

2.9 Maximum Likelihood.

2.10 Monte Carlo Studies.

2.11 Adding Up.

 

3. The Classical Linear Regression Model.

3.1 Textbooks as Catalogs.

3.2 The Five Assumptions.

3.3 The OLS Estimator in the CLR Model.

 

4. Interval Estimation and Hypothesis Testing.

4.1 Introduction.

4.2 Testing a Single Hypothesis: the t Test.

4.3 Testing a Joint Hypothesis: the F Test.

4.4 Interval Estimation for a Parameter Vector.

4.5 LR, W, and LM Statistics.

4.6 Bootstrapping.

 

5. Specification.

5.1 Introduction.

5.2 Three Methodologies.

5.3 General Principles for Specification.

5.4 Misspecification Tests/Diagnostics.

5.5 R2 Again.

 

6. Violating Assumption One: Wrong Regressors, Nonlinearities, and Parameter Inconstancy.

6.1 Introduction.

6.2 Incorrect Set of Independent Variables.

6.3 Nonlinearity.

6.4 Changing Parameter Values.

 

7. Violating Assumption Two: Nonzero Expected Disturbance.

 

8. Violating Assumption Three: Nonspherical Disturbances.

8.1 Introduction.

8.2 Consequences of Violation.

8.3 Heteroskedasticity.

8.4 Autocorrelated Disturbances.

8.5 Generalized Method of Moments.

 

9. Violating Assumption Four: Instrumental Variable Estimation.

9.1 Introduction.

9.2 The IV Estimator.

9.3 IV Issues.

 

10. Violating Assumption Four: Measurement Errors and Autoregression.

10.1 Errors in Variables.

10.2 Autoregression.

 

11. Violating Assumption Four: Simultaneous Equations.

11.1 Introduction.

11.2 Identification.

11.3 Single-equation Methods.

11.4 Systems Methods.

 

12. Violating Assumption Five: Multicollinearity.

12.1 Introduction.

12.2 Consequences.

12.3 Detecting Multicollinearity.

12.4 What to Do.

 

13. Incorporating Extraneous Information.

13.1 Introduction.

13.2 Exact Restrictions.

13.3 Stochastic Restrictions.

13.4 Pre-test Estimators.

13.5 Extraneous Information and MSE.

 

14. The Bayesian Approach.

14.1 Introduction.

14.2 What Is a Bayesian Analysis?.

14.3 Advantages of the Bayesian Approach.

14.4 Overcoming Practitioners' Complaints.

 

15. Dummy Variables.

15.1 Introduction.

15.2 Interpretation.

15.3 Adding Another Qualitative Variable.

15.4 Interacting with Quantitative Variables.

15.5 Observation-specific Dummies.

 

16. Qualitative Dependent Variables.

16.1 Dichotomous Dependent Variables.

16.2 Polychotomous Dependent Variables.

16.3 Ordered Logit/Probit.

16.4 Count Data.

 

17. Limited Dependent Variables.

17.1 Introduction.

17.2 The Tobit Model.

17.3 Sample Selection.

17.4 Duration Models.

 

18. Panel Data.

18.1 Introduction.

18.2 Allowing for Different Intercepts.

18.3 Fixed versus Random Effects.

18.4 Short Run versus Long Run.

18.5 Long, Narrow Panels.

 

19. Time Series Econometrics.

19.1 Introduction.

19.2 ARIMA Models.

19.3 VARs.

19.4 Error-correction Models.

19.5 Testing for Unit Roots.

19.6 Cointegration.

 

20. Forecasting.

20.1 Introduction.

20.2 Causal Forecasting/Econometric Models.

20.3 Time Series Analysis.

20.4 Forecasting Accuracy.

 

21. Robust Estimation.

21.1 Introduction.

21.2 Outliers and Influential Observations.

21.3 Guarding Against Influential Observations.

21.4 Artificial Neural Networks.

21.5 Non-parametric Estimation.

 

22. Applied Econometrics.

22.1 Introduction.

22.2 The Ten Commandments of Applied.

22.3 Getting the Wrong Sign.

22.4 Common Mistakes.

22.5 What Do Practitioners Need to Know?.

 

23. Computational Considerations.

23.1 Introduction.

23.2 Optimizing via a Computer Search.

23.3 Estimating Integrals via Simulation.

23.4 Drawing Observations from Awkward Distributions.

 

General Notes.

Technical Notes.

Appendix A: Sampling Distributions, the.

Foundation of Statistics.

Appendix B: All about Variance.

Appendix C: A Primer on Asymptotics.

Appendix D: Exercises.

Appendix E: Answers to Even-numbered Questions.

Glossary.

Bibliography.

Name Index.

Subject Index