SYLLABUS
Econometrics
Course Description:
This course is designed to be a practical deep dive into Econometrics and Regression Analysis. Regression is one of the most powerful and useful tools when analyzing data. Econometrics, at it core, is about understanding the relationships between variables. In this course, we will go beyond prediction and statistical tests to begin to understand the causal relationship between variables and how to use data to make the best decisions possible.
This course will use Python, a popular high-level computer language that is widely used in finance, consulting, technology, and other parts of the business world.
Students should expect to leave this class with a strong understanding of how Econometrics is used in business and be able to effectively generate their own models in a variety of contexts.
Textbook:
Main text:
Introductory Econometrics: A Modern Approach by Jeffrey Wooldridge
Optional:
Mostly Harmless Econometrics: An Empiricist’s Companion by Joshua Angrist & Jorn-Steffan Pischke
Instructor:
Benjamin Zweig
Email: [email protected]
Office Hours: By appointment
Requirements
All students must have experience with statistics, as this course builds upon that knowledge. We also expect students to be familiar enough with programming to be able to analyze data in python.
Our one requirement is that you must bring a laptop computer to class. It should be your own computer, or at least one you can install new programs on. We will use it constantly in class, writing and correcting short programs.
Course Schedule:
|
|
Topic |
Subtopics |
|
Topic 1 |
Introduction |
● Statistics, Regression, Causality, and why it matters ● Python Review |
|
Topic 2 |
Uses of Regression |
● Prediction and conditional expectation ● Inference ● Decomposition of Variance ● Interpreting Regressions |
|
Topic 3 |
Running Good Regressions |
● Fit ● Unbiasedness and Validity ● Nonlinearities |
|
Topic 4 |
Multiple Regression |
● Interpreting Multiple Regression ● Omitted Variable Bias ● Attenuation Bias |
|
Topic 5 |
Complex Relationships |
● Categorical Variables ● Interactions |
|
Topic 6 |
Flexible Models |
● Nonparametric Models ● Nonlinear Least Squares |
|
Topic 7 |
Discrete Choice Models |
● Binary Dependent Variables ● Multinomial Models ● Ordinal Models |
|
Topic 8 |
Causality |
● Causal Frameworks ● A/B Testing ● Propensity Score Matching |
|
Topic 9 |
Causal Models |
● Instrumental Variables ● Regression Discontinuity |
|
Topic 10 |
Panel Data Models |
● Differences in Differences ● Synthetic Controls |
|
Topic 11 |
Time Series |
● Autoregressive Models |
|
Topic 12 |
Representativeness |
● Sampling Weights ● Quantile Regression |
Deliverables and grades
Graded work includes:
● Problem Sets. There will be several assignments over the course of the semester. They are a great way to develop your skills and come to the following class with questions about what you may not yet understand.
● Exams. Each exam will be non-cumulative but comprehensive. The focus of the exams will test your conceptual understandings of econometrics.
All your work should be clean and professional.
Final grades will be computed from:
Class Participation 20%
Problem Sets 30%
Exams 50%
Policies
Ethics, disabilities, and many other things are governed by NYU and Stern policies. If you have questions about them, please ask.
On graded work: You may discuss assignments with anyone (in fact, we encourage it), but anything you submit, including your code, should be your own. Exams should be entirely your own work.
On disabilities: If you have a qualified disability that requires academic accommodation, please contact the Moses Center for Students with Disabilities (CSD, 212-998-4980) and ask them to send us a letter verifying your registration and outlining the accommodation they recommend. If you need to take an exam at the CSD, you must submit a completed Exam Accommodations Form to them at least one week prior to the scheduled exam time to be assured accommodation.