Introductory Econometrics Econ 399 B1 - Winter 2020
Instructor: Sebastian Fossati Office: Tory 7-11
Email: [email protected]
Website: http://www.ualberta.ca/~sfossati/
Office Hours: TBD (see course website)
Lecture
Tuesday and Thursday 11:00 am to 12:20 pm in T 1 90 (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? (example: are men paid more than women with equivalent levels of education?) (2) How do we measure parameters of scientific interest? (example: how much does one year of schooling raise wages?) (3) What are good methods of forecasting? (example: what will GDP be next year?)
Questions (1) and (2) are the main focus of Econ 399 and Econ 497. Question (3) is covered in Econ 493.
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 281, 282 and 299 or equivalent, and MATH 156 or equivalent.
These pre/corequisites are enforced by the department. If you do not have these pre/corequisites your registration may be cancelled.
Textbooks
Wooldridge (6th edition) will be our main reference (other editions can also be used). The course will follow this textbook closely. 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 Wooldridge).
- Wooldridge, J.M. (2016): Introductory Econometrics: A Modern Approach, 6th Edition.
ISBN: 1-305-27011-8.
- Kleiber, C., and Zeileis, A. (2008): Applied Econometrics with R.
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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.
Lab Sessions
As part of the course you will learn how to use the statistical software R. Lab sessions will pro- vide an introduction to R for successful completion of homework assignments. Lab attendance is highly encouraged.
Grading
The final grade will be based on eight homework assignments (20%, 2.5% each), a midterm exam (40%), 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.
- Midterm Exam: Thursday February 27 (in class). Sample exam questions will be made available on the course website.
- Final Exam: Monday April 20 at 2:00 pm (school schedule). Sample exam questions will be made available on the course website.
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Course Outline
1. What is econometrics? The structure of economic data. (Chapter 1)
2. The simple regression model: Derivation and properties of the ordinary least squares es- timates. Goodness-of-fit. Units of measurement and functional form. Regression through the origin. (Chapter 2)
3. The simple regression model: Inference. Sampling distributions of the OLS estimators.
Testing hypotheses and confidence intervals. Reporting regression results.
4. Multiple regression analysis: Estimation. Mechanics and interpretation of ordinary least squares. Comparison of simple and multiple regression estimates. Goodness-of-fit. Re- gression through the origin. Including irrelevant variable in a regression model. Omitted variable bias. Efficiency of OLS: the Gauss-Markov theorem. (Chapter 3)
5. Multiple regression analysis: Further issues. Data scaling, functional form, goodness-of-fit and selection of regressors. (Chapter 6)
6. Multiple regression analysis: Inference. Sampling distributions of the OLS estimators.
Testing hypotheses and confidence intervals. Reporting regression results. (Chapter 4) 7. Prediction. (Chapter 6)
8. Multiple regression analysis with qualitative information: Binary (or dummy) variables.
Single and multiple dummy independent variables. A binary dependent variable: the linear probability model. (Chapter 7)
9. Heteroskedasticity. Consequences of heteroskedasticity for OLS. Heteroskedasticity-robust inference. Testing for heteroskedasticity. Weighted least squares estimation. (Chapter 8) 10. More on specification and data problems. Functional form misspecification and outlying
observations. (Chapter 9)
11. Serial correlation. Properties of OLS with serially correlated errors. Testing for serial correlation. Correcting for serial correlation with strictly exogenous regressors. Serial correlation-robust inference after OLS. (Chapter 12)
12. Regression analysis with time series data. Trends. Spurious Regression. (Chapter 10)
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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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