A Guide to Econometrics / Edition 6

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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 (do's and don'ts). The 6E contains new chapters on instrumental variables and on computation considerations, more information on GMM and nonparametrics, and an introduction to wavelets.

About the Author:
Peter Kennedy is Professor of Economics at Simon Fraser University

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Editorial Reviews

From the Publisher
"The first edition of this book was a slim, non-technical introduction that would commonly be recommended to students struggling with their main course text. Over the years it has metamorphosed into a substantial volume in its own right, but the basic idea remains; light on technicalities but strong on insights, tips and quirky asides." (Times Higher Educations Supplement, February 2009)
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Product Details

  • ISBN-13: 9781405182584
  • Publisher: Wiley
  • Publication date: 2/25/2008
  • Edition description: REV
  • Edition number: 6
  • Pages: 600
  • Sales rank: 1,195,824
  • Product dimensions: 7.80 (w) x 10.10 (h) x 1.33 (d)

Meet the Author

Peter Kennedy is Professor of Economics at Simon Fraser University. In addition to A Guide to Econometrics, he is author of Macroeconomic Essentials: Understanding Economics in the News, 2e (2000), and is Associate Editor of the International Journal of Forecasting, the Journal of Economic Education, and Economics Bulletin.

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Table of Contents



1. Introduction.

1.1 What is Econometrics?.

1.2 The Disturbance Term.

1.3 Estimates and Estimators.

1.4 Good and Preferred Estimators.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

5. Specification.

5.1 Introduction.

5.2 Three Methodologies.

5.3 General Principles for Specification.

5.4 Misspecification Tests/Diagnostics.

5.5 R2 Again.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

7. Violating Assumption Two: Nonzero ExpectedDisturbance.

General Notes.

8. Violating Assumption Three: NonsphericalDisturbances.

8.1 Introduction.

8.2 Consequences of Violation.

8.3 Heteroskedasticity.

8.4 Autocorrelated Disturbances.

8.5 Generalized Method of Moments.

General Notes.

Technical Notes.

9. Violating Assumption Four: Instrumental VariableEstimation.

9.1 Introduction.

9.2 The IV Estimator.

9.3 IV Issues.

General Notes.

Technical Notes.

10. Violating Assumption Four: Measurement Errors andAutoregression.

10.1 Errors in Variables.

10.2 Autoregression.

General Notes.

Technical Notes.

11. Violating Assumption Four: SimultaneousEquations.

11.1 Introduction.

11.2 Identification.

11.3 Single-equation Methods.

11.4 Systems Methods.

General Notes.

Technical Notes.

12. Violating Assumption Five: Multicollinearity.

12.1 Introduction.

12.2 Consequences.

12.3 Detecting Multicollinearity.

12.4 What to Do.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

16. Qualitative Dependent Variables.

16.1 Dichotomous Dependent Variables.

16.2 Polychotomous Dependent Variables.

16.3 Ordered Logit/Probit.

16.4 Count Data.

General Notes.

Technical Notes.

17. Limited Dependent Variables.

17.1 Introduction.

17.2 The Tobit Model.

17.3 Sample Selection.

17.4 Duration Models.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

20. Forecasting.

20.1 Introduction.

20.2 Causal Forecasting/Econometric Models.

20.3 Time Series Analysis.

20.4 Forecasting Accuracy.

General Notes.

Technical Notes.

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.

General Notes.

Technical Notes.

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?.

General Notes.

Technical Notes.

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.



Name Index.

Subject Index

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