Econometric Methods
Econ 497 B1 - Winter 2019
Instructor: Sebastian Fossati Office: Tory 7-11
Email: [email protected]
Website: http://www.ualberta.ca/~sfossati/
Office Hours: Wednesday 2:00 to 4:00 pm
Lecture
Tuesday and Thursday 2:00 pm to 3:20 pm in T B 113 (Tory Building).
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? Questions (1) and (2) are the main focus of Econ 399 and Econ 497. Question (3) is covered in Econ 493.
The emphasis of Econ 497 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. (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 (other 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).
- Verbeek, M. (2012): A Guide to Modern Econometrics, 4th Edition.
- Kleiber, C., and Zeileis, A. (2008): Applied Econometrics with R.
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Additional Textbooks
You may also find the following textbooks useful (other editions can also be used).
- Wooldridge, J.M. (2013): Introductory Econometrics: A Modern Approach, 5th Edition.
- James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013): An Introduction to Statistical Learning: with Applications in R.
- Greene, W.H. (2012): Econometric Analysis, 7th Edition.
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 can be downloaded for free from the university library.
Grading
The final grade will be based on four homework assignments (10%, 2.5% each), an applied paper (30%), a midterm exam (30%), and a final exam (30%). 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 the associated proposal) will have cumulative late penalties. The report must be completed in order to receive credit for this course. Detailed instructions will be distributed later.
Proposal due date: Wednesday February 27 at 11:59 am.
Final report due date: Wednesday April 10 at 11:59 am.
- Midterm Exam: Thursday February 14 (in class). Sample exam questions will be made available on the course website.
- Final Exam: Tuesday April 23 at 2:00 pm (school schedule). Sample exam questions will be made available on the course website.
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Course Outline
Note: JWHT denotes “James, Witten, Hastie, and Tibshirani”.
0. Review of probability, statistics, and matrix algebra (not covered in class) Verbeek: app. A, B
Wooldridge: app. B, C, D
1. Multiple regression analysis: estimation and inference Verbeek: ch. 2
JWHT: ch. 3.1, 3.2
Wooldridge: app. E, ch. 3–5
2. Interpreting and comparing regression models Verbeek: ch. 3.1, 3.3–3.6
JWHT: ch. 3.3, 3.4 Wooldridge: ch. 6, 9.1
3. Heteroskedasticity and autocorrelation Verbeek: ch. 4
Wooldridge: ch. 8, 12
4. Model selection with a small number of regressors Verbeek: ch. 3.2
JWHT: ch. 5.1, 6.1, 6.5
5. Model selection with a large number of regressors JWHT: ch. 6.2–6.4, 6.6, 6.7
6. Endogeneity and instrumental variables Verbeek: ch. 5.1–5.4
Wooldridge: ch. 15.1–15.5
7. Experiments and quasi-experiments TBD
8. Models with limited dependent variables Verbeek: ch. 7.1, 7.4, 7.5
Wooldridge: ch. 17.1, 17.2
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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.
Accessibility Resources (formerly Student Accessibility Services)
If you have a condition that may require some classroom or exam modifications, please contact Accessibility Resources to obtain a determination as to what accommodations should be made.
Academic Success Centre
The Academic Success Centre offers a variety of learning resources, including a variety of work- shops in learning effective study and exam strategies.
Centre for Writers
The Centre for Writers offers free one-on-one writing coaching to all students. Students can request consultation for a writing project at any stage of development.
Notes
Policy about course outlines can be found in Course Requirements, Evaluation Procedures and Grading in 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.governance.ualberta.ca) and avoid any behaviour that could poten- tially 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, digital or otherwise, of lectures, labs, seminars or any other teaching environment by students is allowed only with the prior written consent of the instructor or as part of an approved accommo- dation plan. Student or instructor content, digital or otherwise, created and/or used within the context of the course 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 content author(s).
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