HelpFeedback
Cameron, Econometrics 1e
Information Center
Overview
Key Features
Table of Contents
Preface
Sample Chapter
About the Author
Reviewer Comments
Request Lecturer Copy
Request Password
Buy the Book
PageOut
Adobe Reader
Feedback


Student Edition
Instructor Edition
Econometrics

Samuel Cameron, University of Bradford

ISBN: 0077104285
Copyright year: 2005

Table of Contents



1. Introducing Econometrics.

Learning Objectives

1.1 The role of this chapter and the nature of this book.
1.2 Question 1: So What Is Econometrics?
1.3 Question 2: Who uses Econometrics and Why?
1.4 Question 3: Do You Have to be an econometrician to be do econometrics?
1.5 Question 4: Do users of econometrics disagree?
1.6 Question 5: Could we do without econometrics?
1.7 Question 6: How did modern econometrics start?
1.8 Question 7: Where is econometrics going to?
1.9 Question 8: How do I use this book?
1.10 Question 9: What should I know and be able to do when I have finished?
1.11 Question 10: What Software Should I Use do Econometrics?
1.12 What Comes Next?

Do's and Don'ts
Exercises
References

2. Statistical Testing and Modelling. The Basics.

Learning Objectives

2.1 Types of data.
2.2 Sampling
2.3 The uses of the concept of probability.
2.4 Hypothesis testing and probability distributions.
2.5 Hypothesis testing: how to write it down properly.
2.6 How do we decide on the significance level.
2.7 Covariance and correlation coefficients.
2.8 Conclusion

Do's and Don'ts
Exercises
References

3. The Classical Linear Regression Model Introduced.

Learning Objectives

3.1 Bivariate Regression.
3.2 The role of the disturbance term.
3.3 Classical assumptions of the disturbance term.
3.4 OLS estimators: what makes them blue and where do they come from.
3.5 Hypothesis Testing in the CLRM.
3.6 A note on the R2 statistic.
3.7 What is the Gauss-Markov Theorem?
3.8 Examples of bivariate regression.
3.9 From bivariate to multiple regression.
3.10 Review of published studies.
3.11 Conclusion

Do's and Don'ts
Exercises
References

4. Preparing and Using Data.

Learning Objectives

4.1 Introduction
4.2 Possible Problems with the data set.
4.3 How will my data arrive and what do I do with it?
4.4 Descriptive Exploration of Your Data.
4.5 Running a Regression.
4.6 Interpreting your results.
4.7 Additional Treatment of your regression output.
4.8 Turning your regression output into a presentation or article.
4.9 Case Study. The Willingness to Pay Study.
4.10 Collecting data: some further reflections.
4.11 Conclusion

Do's and Don'ts
Exercises
References

Appendix: Questionnaire Design

5. What do all these tests and statistics mean?

Learning Objectives

5.1 Introduction. Typical test statistics in computer output.
5.2 Telling the story of the regression coefficients: the use of elasticities.
5.3 The construction and use of ‘F’ tests in regression.
5.4 Adjusting the R squared: how and why?
5.5 Be careful with all R squareds.
5.6 Basic Econometric Forecasting.
5.7 Review Studies.
5.8 Conclusion

Do's and Don'ts
Exercises
References

6. Making Regression Analysis More Useful I: Transformations

Learning Objectives

6.1 Introduction
6.2 Non-Linearity as a problem.
6.3 Making interaction terms.
6.4 What if we wrongly choose a linear model?
6.5 How do we estimate non-linear equations using OLS?
6.6 Choice of functional form and a word on R2.
6.7 Some other transformations for non-linearity.
6.8 Lag and lead transformations to produce dynamic models.
6.9 Review Studies.
6.10 Conclusion

Do's and Don'ts
Exercises
References

7. Making Regression Analysis More Useful II: Dummies and Trends

Learning Objectives

7.1 Introduction: making a dummy.
7.2 Why do we use dummies?
7.3 Are Dummies always 0-1 Variables?
7.4 Where do I get my dummies from? The seasons are an obvious answer.
7.5 The slope dummy.
7.6 Review Studies.
7.7 Another use for dummies: forecast evaluations.
7.8 Time trends.
7.9 Another use for dummies: spline functions to produce non-linearity.
7.10 Conclusion

Do's and Don'ts
Exercises
Study Questions
References

8. Predicting and Explaining Discrete Events: Logit and Probit Models

Learning Objectives

8.1 Introduction
8.2 Predicting Probabilities Using OLS.
8.3 Should We Use OLS to explain probabilities:? The Alternatives.
8.4 The Rationale for Logit and Probit Models.
8.5 Basic Use and Interpretation of Logit and Probit Models.
8.6 Review Studies.
8.7 More Complex Probabilistic Models.
8.8 Problems in Interpreting and Testing Logit and Probit Models.
8.9 Conclusion

Do's and Don'ts
Exercises
References

9. More Tests: Diagnosing the results of basic models.

Learning Objectives

9.1 Introduction
9.2 Testing for Outliers.
9.3 Testing for Normality.
9.4 Testing for Stability.
9.5 Testing for Functional Form.
9.6 Testing for Heteroscedasticity.
9.7 Testing for Autocorrelation.
9.8 Testing for Bias.
9.9 Review Studies.
9.10 Conclusion

Do's and Don'ts
Exercises
References

10. Multicollinearity. Serious Worry or Minor Nuisance?

Learning Objectives

10.1 Introduction
10.2 What is Multicollinearity?
10.3 Consequences of Multicollinearity.
10.4 Exploring for Multicollinearity.
10.5 A Proposed Test for Multicollinearity.
10.6 Causes of Multicollinearity.
10.7 Should I Worry About Multicollinearity Or Not?
10.8 Review Studies.
10.9 Conclusion

Do's and Don'ts
Exercises
References

11. Problem Solving: Heteroscedasticity.

Learning Objectives

11.1 Introduction
11.2 Consequences of Heteroscedasticity.
11.3 Solving the problem of hetroscedasticity: I. Use Transformed Data To Obtain Weighted Least Squares (WLS) estimates.
11.4 Solving the problem of heteroscedascity:II. Use Corrected Standard Errors.
11.5 Causes of Heteroscedasticity.
11.6 Should I correct for heteroscedasicity?
11.7 Review Studies.
11.8 Conclusion

Do's and Don'ts
Exercises
References

12. Multiple equation models: Specification and the Identification Problem.

Learning Objectives

12.1 Introduction
12.2 Do we need more than one equation in our model?
12.3 Handling systems of equations.
12.4 Identification : the order condition.
12.5 Rules for identification continued: order conditions n a larger model.
12.6 Some Important Questions.
12.7 Conclusion

Do's and Don'ts
Exercises
References

13. Multiple equation models: Methods of Estimation.

Learning Objectives

13.1 Introduction- Identification Again.
13.2 Using OLS to estimate a multiple equation model: the bias problem.
13.3 2SLS: modified OLS.
13.4 The Relationship Between 2SLS and IV.
13.5 Interpretation of the results of 2SLS/IV estimates: some examples.
13.6 Exogeneity Testing.
13.7 Review Studies.
13.8 Other types of systems model: SUR estimation and Path Models.
13.9 More advanced methods I: 3SLS.
13.10 More advanced methods II: FIML.
13.11 Can we test for identification?
13.12 A recap: Four Problems.
13.13 Conclusion

Do's and Don'ts
Exercises
References

14. Problem Solving: Time-Series.

Learning Objectives

14.1 Introduction
14.2 What we already know about time series.
14,3 The problem of Spurious Regression.
14.4 Avoiding spurious regression: pre testing I; unit roots and other tests.
14.5 Cointegration: definition and tesing.
14.6 Formulating a time-series econometrics model: Engle-Granger Two Step Model.
14.7 Review Studies.
14.8 Some reservations on the above methods.
14.9 Granger Causality.
14.10 Time series analysis as opposed to (structural) economic analysis of time series.
14.11 More sophisticated approaches to cointegration testing and system modeling.
14.12 Conclusion

Do's and Don'ts
Exercises
References

15. Conclusion.

Learning Objectives

15.1 Introduction: Time to review the subject field of econometrics. Data and technique.
15.2 Chapter 1 Revisited: Methodology: Bayesian Econometrics.
15.3 Chapter 2 Revisited: The significance of significance testing.
15.4 Chapter 3 Revisited: The basic regression equation. Are Single equation Models of Any Use?
15.5 Chapter 4 Revisited: Measurement.
15.6 Chapter 5 revisited: Goodness of Fit; Model Selection Tests.
15.7 Chapter 6 Revisited: Simple Dynamic Models and Non-Linearity.
15.8 Chapter 7 Revisited: Are artificial variable safe to play with?
15.9 Chapter 8 Revisited: Limited dependent variable models.
15.10 Chapter 9 The Reliability of Diagnostic Tests.
15.11 Chapter 10 Revisited : Multicollinearity.
15.12 Chapter 11 Revisited: Heteroscedasticity.
15.13 Chapter 12 Revisited : Identification.
15.14 Chapter 13 Revisited: Systems of Equations.
15.15 Chapter 14 Revisited : Time series Analysis.
15.16 Final Words - What Use is Econometrics?

Exercises
Further Reading

Appendices

I. Blank Template of Summary Sheet for Review Studies.
II. Brief Description of the Excel files of Data Sets.
III. The Notion of Causality.

To obtain a lecturer login to the Online Learning Centres, ask your local sales representative. If you're a lecturer thinking about adopting this textbook, request a lecturer copy for review.