A teaching statement is available here .
Senior honors thesis workshop
Fall 2023 $\cdot$ Spring 2024
In this role, I designed and administered workshops on applied research methods and project-oriented workflows for senior honors thesis undergraduate students. My responsibilities also included serving as a departmental consultant to economics students conducting empirical research (namely, undergraduate and master’s thesis students and part-time research assistants), meeting one-on-one for 2-8 hours each week. Some prepared materials are enumerated here.
Student feedback
- "Matthew is very organized, knowledgeable, and accessible. Even though he’s not a specialist in my area of research, he went above and beyond in familiarizing himself with my data, and he’s an expert at coding."
Introduction to econometrics
Fall 2020 $\cdot$ Spring 2021 $\cdot$ Fall 2021 $\cdot$ Summer 2023
ECON-UN3412 introduces students to multiple regression and related methods for analyzing data in economics and related disciplines. Additional topics include regression with discrete random variables, instrumental variables regression, analysis of random experiments and quasi-experiments, and regression with time series data. Students will learn how to conduct and critique empirical studies in economics and related fields. Accordingly, the emphasis of the course is on empirical applications.
Text: Introduction to Econometrics, James Stock and Mark Watson (2019, 2020)
Columbia traditionally teaches its undergraduates econometrics using Stata. I took the initiative to prepare supplementary material and solutions to provide students the option to instead use R, a freely available open-source alternative. After some initial trepidation about departing from the official software of instruction, students seemed to find its object-oriented programming more intuitive and appreciated its accessibility beyond the semester course. I continued teaching the course in R and Stata simultaneously over four semesters.
Select student feedback
- "Literally the best TA I’ve ever had. Always open to answering questions. Has the best study guides for learning R which was a relatively new language for me. I could not have done it without him." — Fall 2021
- "Matt is a great TA who went above and beyond by making additional study notes that explained very clearly what some of the most difficult concepts meant." — Fall 2021
- "He was extremely helpful and his teaching may be improved only by posting video recordings of his recitations. His strength is posting really really clear notes on how to do the course in R." — Fall 2021
- "Matt is a great TA! As a student who used R, I depended heavily on Matt’s notes. He was always super responsive and helped us whenever we needed." — Spring 2021
- "He is very organized and helpful. He would prepare weekly R recitation notes and share them, which were a great help for problem sets." — Spring 2021
- "Matthew did a nice job going over practice problems during each recitation as well as explaining the significance of the concepts we were learning." — Fall 2020
- "Great TA. He goes through the Stata examples in detail. His solutions to the examples are clear and straightforward. Super intelligent! And he makes his recordings available. Thank you!" — Fall 2020
- "Saved me for my midterm. Great real world examples in recitations." — Fall 2020
Below are weekly guides produced for the Summer 2023 iteration of my classes, which is accelerated and condensed relative to the regular semester course. These materials include a subset of practice problems but exclude material prepared for problem sets, exams, practice exams, and their solutions. Most datasets referenced are available for free here.
Instruction here presumes no prior experience with programming other than installation of R and RStudio. It introduces and makes use of R Notebooks saved as R Markdown (.Rmd) files, a convenient way of integrating in-line R scripting with intuitive and customizable word processing to produce handsome problem sets and reports either as pdfs or HTML files. If you’re following on your own, open the Rmd files in RStudio to see the input and the pdf files to see what the resultant output looks like. If I were to update this material, I’d integrate .Rprofiles into the problem set pipeline to ease students’ experience further.
1. Introduction to R and R Notebooks | RMD
2. Multicollinearity, joint hypothesis testing, and the tidyverse | RMD
4. Nonlinear regression | RMD
5. Panel data methods and binary dependent variables | RMD
7. Instrumental variables and quasi-experiments | RMD
8. Big data, time series and dynamic causal effects | RMD
Intermediate microeconomics
Spring 2022 $\cdot$ Fall 2022
The purpose of this course is to offer a solid, intermediate-level training in theoretical microeconomics. We will try to achieve a more in-depth understanding of how the standard theoretical models of microeconomics work. The course consists of three parts. We will start out by analyzing consumer decision-making. We then turn to the behavior of firms. Finally, the third part of the course studies the interaction of consumers and firms in goods markets.
Text: Intermediate Microeconomics with Calculus, Hal Varian (2014)
Select student feedback
- "William Matthew Davis is one of the smartest people I have ever met. He is ridiculously prepared and extremely knowledgeable. The amount of resources which he gifts to students is astounding, and I am so grateful for his help." — Fall 2022
- "Really accessible guy who seems to really care about his TA work!" — Fall 2022
- "Very clear TA with an emphasis on student experience" — Fall 2022
- "I love him! Posted all his recitations online, which really helps." — Spring 2022
The slides below are weekly elaborations on a specific newly introduced concept and so do not represent comprehensive coverage of the course material. They’re also not necessarily self-contained; I sometimes supplemented my slides with blackboard work and conversation with students, which is not always captured in the annotations. Course material is also instructor-specific; if you’re reading this as a Columbia student taking the same course, it may not correspond to the treatment or selection of topics your particular instructor chooses to cover. For example, compared to my Spring 2022 coverage, the following material from Fall 2022 covers a wider range of topics but omits some material such as the method of Lagrange multipliers.
2. Hicksian demand and special well-behaved preferences
3. Comparative statics and Slutsky decomposition of price effects
4. Hicksian decomposition of price effects and introduction to welfare
5. Producer Theory I: Cost minimization
6. Producer Theory II: Profit maximization under perfect competition
8. Exchange economies and the Edgeworth box
9. Models of monopolistic and oligopolistic competition
10. Comparing models of oligopolistic competition
A1. Running log of student emails and corrections to weekly slides
A2. Week 8: by student request, deriving IC equations for accurate graphing
A3. Week 10: High-level summary and takeaways from the course
The global economy
Spring 2023
This international economics elective is targeted towards non-economics majors, focusing on current events and the increasing international interdependence of the world economy. Topics incude: (i) why countries trade, what goods and services will be traded, how the gains from trade are distributed and the tools of commercial policy; (ii) the movement of labor and capital across borders; value of transnational countries and production processes across countries; (iii) international finance issues including exchange rates, balance of payments and open economy macroeconomic adjustment.
Select student feedback
- "Great TA. He was always available for students, and made sure all the course material was understood"
- "His teaching during recitation is very clear and organized and easy to follow."
I am not permitted to share material prepared for this course