A repository with the Python resources from Summer 2019 ARE 106 taught by Aleksandr Michuda
Visit my page for materials for BITSS Transparency and Reproducibility in Python: amichuda.com
Also, thanks to my amazing TA: Bret Stevens
ARE 106: Econometric Theory and Applications
Instructor: Aleksandr Michuda ([email protected])
Office Hours: Tuesday 2-4 or by appt (SSH TA Room)
TAs: Bret Stevens ([email protected])
Discussion: Thurs 2:10-3:50 and 4:10-5:50 (Olson 141)
Office Hours: TBD
Class Website: Visit the canvas site for this course regularly for handouts and announcements, as well supplementary resources.
UCD Student resources: For answers to questions about academic support, health and wellness, career and internships, or campus community, go to https://ebeler.faculty.ucdavis.edu/resources/faq-student-resources/
Course Objectives: You have all studied economic theory and statistics. This course brings together economic theory, mathematical tools, and statistics in order to do three things:
(1) Estimate economic relationships and predict economic outcomes, e.g., how does a change in price affect consumption? Can we predict your future earnings based on the education you are getting now?
(2) Generalize from a sample of data to whole populations, and
(3) Test hypotheses about cause and effect.
Additionally, our objective here will also be to understand how to accomplish these goals using computer programming. This will involve becoming more familiar with the command line on a computer and the basic ins and outs about correct way to code. We will be using Python for this course, which is a valuable skill to have in and of itself.
Please refer to the installation guide here to install the required software:
https://lectures.quantecon.org/py/getting_started.html#jupyter-notebooks
Prerequisites: ARE 100A, STA 103.
Lectures: Monday, Tuesday, Wednesday 4:10-5:50 in Hoagland Hall 168.
Computing: We will use Python for the computing in the class. This will involve installing and running the Anaconda Python Distribution. Homework will be done using Jupyter Notebooks, which is a useful tool for visualizing code and running estimations.
We will have several classes dedicated to learning how to code, so bringing a laptop on those days is recommended.
Homework and exams may also include questions on programming concepts as well as programming logic.
Reading: The textbook is Essentials of Applied Econometrics, by Aaron Smith and J. Edward Taylor. Oakland: University of California Press. Available on-line through UC Press: http://www.ucpress.edu/book.php?isbn=9780520288331
eBook Access: Rather than buying the physical copy, you also have the option of buying the eBook version of the textbook. Check the canvas website for access.
Statistics Review: I assume that you remember and understand the statistics you learned in Stat 103 (or equivalent). Now is a good time to review that material. There are also reviews of stats for econometrics available on-line. A couple of examples are:
http://www.ssc.wisc.edu/~ctaber/410/statrev.pdf
http://www.dummies.com/how-to/content/statistics-for-dummies-cheat-sheet.html
Assessment: There will be five homework assignments, a midterm, and a final exam. The midterm is scheduled for Monday, August 26th from 4:10-5:50 in Hoagland Hall 168 (i.e. during our regular class time) and the final will be on the last class that we meet, September 11th (during regular class time as well). This exam date is not negotiable. No consultation with any people, books, notes or other resources will be permitted during the exams. Any violations of this, or any other policy stated in this syllabus, will be dealt with according to University policy.
There are a total of 100 points available in this class. Your letter grade will be determined from your score out of 100.
Homeworks (50 points Total)
I will give you five homework assignments throughout the course. To get full credit on a question, you have to put in a good faith attempt on the question, or no credit will be given. These will include questions that will be pertinent for what we are doing and will give you practice for the exams.
The credit for the first homework, you will receive automatically, and it will be to set up Anaconda, Python and Jupyter notebooks, as well as any package dependencies that you might need so that you can do your analysis and homework through Jupyter.
Homework assignments will be submitted in Jupyter notebook format and will not be late. Since you have a full week to come to us and figure out how to make Jupyter work on your computer, failure to submit the homework on time and in Jupyter format will result in a missed homework.
The first homework assignment will be worth 10 points on your grade. The other four homeworks will be worth 13.5 points each. You can only receive 50 points through the submission of homework assignments, which presents an opportunity for you. If you do well enough on the three homework assignments, you might not need to do the last homework.
Midterm and Final (25 points each -> 50 points Total)
The midterm will be worth a maximum of 25 points and the final exam will be worth 25. I will allow you to use your score on the final exam in place of your midterm (if this improves your overall score). This policy allows you to make up for a bad midterm by doing well on the final.
On the pages that follow, there is a course calendar. It has an approximate lecture schedule as well as exam dates and due dates for homeworks. It should be a useful resource for you. Please contact me if you have any questions, complaints or compliments about the class.
Aleksandr Michuda
August 2019
| MON | TUE | WED | THUR | FRI | ||
| AUG | 5 | 6 | 7 | 8 | 9 | |
|
Lecture 1 Introduction and Stats Review
Reading: Ch. 1 |
Lecture 2 Simple Regression
Reading: Ch. 2 |
Lecture 3 Introduction to Coding and Python |
WEEK 1 |
|||
| AUG | 12 | 13 | 14 | 15 | 16 | |
|
Lecture 4 Multiple Regression Reading: Ch. 3 HW 1 Due |
Lecture 5 Multiple Regression Reading: Ch. 3 |
Lecture 6 Python and Data |
WEEK 2 |
|||
| AUG | 19 | 20 | 21 | 22 | 23 | |
|
Lecture 7 Generalizing from a Sample Reading: Ch. 4 HW 2 Due |
Lecture 8 Properties of Estimators
Reading: Ch. 5 |
Lecture 9 Properties of Estimators
Reading: Ch. 5 |
WEEK 3 |
|||
| AUG | 26 | 27 | 28 | 29 | 30 | |
| Midterm Exam |
Lecture 10 Hypothesis Tests and Confidence Intervals Reading: Ch. 6 HW 3 Due |
Lecture 11 Predicting in a Nonlinear World Reading: Ch. 7 |
WEEK 4 |
|||
| SEP | 2 | 3 | 4 | 5 | 6 | |
| Labor Day (No Class) |
Lecture 12 Heteroskedasticity Reading: Ch. 8 HW 4 Due |
Lecture 13 Sample Selection Bias Reading: Ch. 10 |
WEEK 5 |
|||
| SEP | 9 | 10 | 11 | 12 | 13 | |
|
Lecture 14 Identifying Causation Reading: Ch. 11 HW 5 Due |
Lecture 15 Instrumental Variables
Reading: Ch. 12 |
Final Exam |
WEEK 6 |