Econometric Methods
Econ 497 B1 - Winter 2015
Instructor: Sebastian Fossati Office: Tory 7-11
Email: [email protected]
Website: http://www.ualberta.ca/~sfossati/
Office Hours: Thursday 3:30-4:30 pm
Lecture
Tuesday and Thursday 2:00 pm to 3:20 pm in Tory B-113.
Course Description
Econometrics is the study of statistics as applied in economics. We are interested in answering three kinds of questions. (1) How do we test a given scientific hypothesis? (2) How do we measure parameters of scientific interest? (3) What are good methods of forecasting? The main emphasis is on theory and application of regression methods in a single equation context. Matrix algebra is used extensively.
Learning Goals
The main tool we learn is multiple regression analysis. By the end of the course, students should be able to: (1) Understand, interpret, and implement regression models and related statistical techniques (models with limited dependent variables, time series model, panel data models). (2) Know the limitations and pitfalls of regression methods. (3) Be able to present the findings of a statistical investigation clearly and concisely.
Course Prerequisites and Corequisites
Prerequisites: ECON 386, ECON 387, and ECON 399 (or equivalent).
Corequisites: ECON 481, and ECON 482.
These pre/corequisites are enforced by the department. If you do not have these pre/corequisites your registration may be cancelled.
Textbooks
Verbeek (2012, 4th edition) will be our main reference for the theoretical content of the course (earlier editions can also be used). In addition, Kleiber and Zeileis (2008) will be our reference for econometric computing with R (this is not an econometrics textbook, so it is not a substitute for Verbeek). Kleiber and Zeileis (2008) is available as a free download at the UofA library.
- Verbeek, M. (2012): A Guide to Modern Econometrics, Fourth Edition. Wiley.
ISBN: 978-1-119-95167-4.
- Kleiber, C., and Zeileis, A. (2008): Applied Econometrics with R. Springer.
ISBN: 978-0-387-77316-2.
Additional Textbooks
You may also find the following textbooks useful (earlier editions can also be used).
- Greene, W.H. (2012): Econometric Analysis, 7th Edition. Prentice-Hall.
- Johnston, J., and DiNardo, J. (1997): Econometric Methods, 4th Edition. McGraw-Hill.
- Wooldridge, J.M. (2013): Introductory Econometrics: A Modern Approach, 5th Edition.
South-Western.
Econometrics Package
R is used extensively in the course. R is a free software environment for statistical computing and graphics (http://www.r-project.org). The course website has links to some R manuals for beginners and the Use R! series of books, all available for free (links provided on the course website). These books will help you get started with R.
- Zuur, A.F., Ieno, E.N., and Meesters, E. (2009), A Beginner’s Guide to R. Springer.
- Kleiber, C., and Zeileis, A. (2008): Applied Econometrics with R. Springer.
Grading
The final grade will be based on four homework assignments (10%, 2.5% each), an applied paper (25%), a midterm exam (25%), and a final exam (40%). Further details will be provided as assignments are distributed. Regular class participation is expected. For this course, no extra credits are available.
Grades reflect judgments of student achievement made by instructors. These judgments are based on a combination of absolute achievement and relative performance in a class.
Special notes:
- Homework assignments will be a combination of computer problems using R and analytical problems. Everyone must turn in their own assignments, but collaboration is permitted.
Collaboration on the computer problems is encouraged. Late homework assignments will not be accepted. Solutions will follow after the assignments are handed in.
- The applied paper will be a short length (under 20 pages) paper. The paper (and associated proposal) will have cumulative late penalties. The applied paper must be completed in order to receive credit for this course. Instructions will be distributed later.
Proposal due date: Friday February 6 at 11:59 am.
Final paper due date: Wednesday April 15 at 11:59 am.
- Midterm Exam: Thursday February 26 (in class). We will hold a review session on Tuesday February 24 (in class). Sample exam questions will be made available on the course website.
- Final Exam: Tuesday April 21 at 2:00 pm (school schedule). Although the final exam will be cumulative, the focus will be on topics covered after the midterm. Sample exam questions will be made available on the course website.
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Missed / Deferred Exams
A student who misses the midterm exam because of incapacitating illness, severe domestic afflic- tion or other compelling reason (including religious conviction) may have the percentage weight of the midterm exam transferred to the final exam. The student’s final exam, however, will be different from that for the rest of the class. In this case, the final will contain some questions covering the material pertinent to the missed midterm exam.
A student who misses the final exam because of incapacitating illness, severe domestic affliction or other compelling reason (including religious conviction) may apply for a deferred final exam.
Students seeking a deferred exam need to apply to their own Faculty. The instructor does not have the authority to approve such applications.
More information can be found in §23.3 of the University Calendar.
Course Outline
1. Review of Probability and Matrix Algebra Verbeek: app. A, B
Wooldridge: app. B, C, D
Johnston and DiNardo: app. A, B
2. Multiple Regression Analysis: Estimation and Inference Verbeek: ch. 2
Wooldridge: app. E, ch. 3–5 Johnston and DiNardo: ch. 3
3. Interpreting and Comparing Regression Models Verbeek: ch. 3
Wooldridge: ch. 6, 9.1
Johnston and DiNardo: ch. 4, 8.5
4. Heteroskedasticity and Autocorrelation Verbeek: ch. 4
Wooldridge: ch. 8, 12
Johnston and DiNardo: ch. 6
5. Endogeneity and Instrumental Variables Verbeek: ch. 5.1–5.4
Wooldridge: ch. 15.1–15.5 Johnston and DiNardo: ch. 5.5
6. Maximum Likelihood Estimation Verbeek: ch. 6
Johnston and DiNardo: ch. 5.1–5.4
7. Models with Limited Dependent Variables Verbeek: ch. 7.1, 7.4, 7.5
Wooldridge: ch. 17.1, 17.2
Johnston and DiNardo: ch. 13.1–13.6, 13.10 8. Time Series Models
Verbeek: ch. 8, 9.1–9.3
Wooldridge: ch. 11.1, 11.2, 18
Johnston and DiNardo: ch. 7, 8.1–8.4 9. Panel Data Models
Verbeek: ch. 10.1–10.3 Wooldridge: ch. 13, 14
Johnston and DiNardo: ch. 12
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Notes
Policy about course outlines can be found in§23.4(2) of the University Calendar.
The University of Alberta is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at www.ualberta.ca/secretariat/appeals.htm) and avoid any behaviour which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.
Audio or video recording of lectures, labs, seminars or any other teaching environment by students is allowed only with the prior written consent of the instructor or as a part of an approved accommodation plan. Recorded material is to be used solely for personal study, and is not to be used or distributed for any other purpose without prior written consent from the instructor.