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A Guide to Econometricsby Peter Kennedy
Synopses & Reviews
This is the PERFECT supplement for all econometrics classes--from a strong first undergraduate course to a first master's or PhD course. A proven invaluable aide to students at all levels because it allows them to make sense of what is happening in their econometrics textbook and classroom, this remarkable book presents the logic of econometics without the formulas, providing intuition, skepticism, insights, humor, and practical advice. The sixth edition contains new chapters on instrumental variables, computation considerations, and much more information on GMM, nonparametrics, and an introduction to wavelets.
Book News Annotation:
Kennedy (economics, Simon Fraser U.) presents a revised reference text for undergraduate, masters, and Ph.D. students. Serving as a supplement to econometrics textbooks, it offers an overview of the subject and provides an intuitive feel for its concepts and techniques. The sixth edition has been updated and revised throughout to improve the explanation of numerous topics, and also features a new chapter on instrumental variable estimation and a new chapter on computational considerations. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)
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.
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.
About 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.
Table of Contents
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.
2.2 Computational Cost.
2.3 Least Squares.
2.4 Highest R2.
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.
4.2 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.
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.
6.2 Incorrect Set of Independent Variables.
6.4 Changing Parameter Values.
7. Violating Assumption Two: Nonzero Expected Disturbance.
8. Violating Assumption Three: Nonspherical Disturbances.
8.2 Consequences of Violation.
8.4 Autocorrelated Disturbances.
8.5 Generalized Method of Moments.
9. Violating Assumption Four: Instrumental Variable Estimation.
9.2 The IV Estimator.
9.3 IV Issues.
10. Violating Assumption Four: Measurement Errors and Autoregression.
10.1 Errors in Variables.
11. Violating Assumption Four: Simultaneous Equations.
11.3 Single-equation Methods.
11.4 Systems Methods.
12. Violating Assumption Five: Multicollinearity.
12.3 Detecting Multicollinearity.
12.4 What to Do.
13. Incorporating Extraneous Information.
13.2 Exact Restrictions.
13.3 Stochastic Restrictions.
13.4 Pre-test Estimators.
13.5 Extraneous Information and MSE.
14. The Bayesian Approach.
14.2 What Is a Bayesian Analysis?.
14.3 Advantages of the Bayesian Approach.
14.4 Overcoming Practitioners’ Complaints.
15. Dummy Variables.
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.
17.2 The Tobit Model.
17.3 Sample Selection.
17.4 Duration Models.
18. Panel Data.
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.
19.2 ARIMA Models.
19.4 Error-correction Models.
19.5 Testing for Unit Roots.
20.2 Causal Forecasting/Econometric Models.
20.3 Time Series Analysis.
20.4 Forecasting Accuracy.
21. Robust Estimation.
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.
22.2 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.
23.2 Optimizing via a Computer Search.
23.3 Estimating Integrals via Simulation.
23.4 Drawing Observations from Awkward Distributions.
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.
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