SJSU classes started today on campus in San Jose, and online for our SJSU Online Economics B.A. students. Our SJSU Online program launched last Spring, and today we're welcoming our third cohort. In this post I'd like to share a little about my role in the development of the curriculum and content of our SJSU Online Economics B.A. courses, focusing on our Introduction to Econometrics and Research Methods course, ECON 103a.
In addition to being department chair, and serving as the program coordinator during its development and launch, I also teach in this program. This semester I'm teaching Introduction to Econometrics and Research Methods (ECON 103a). I'm really excited about this, because I've spent my entire career so far, over 15 years at SJSU, building content for intro metrics, and this is the first time I can use my latest teaching resource, my 2021 book Data and the American Dream.
In prepping the course for this semester, I also remembered why I liked using the 2014 book Mastering Metrics so much. And I also was really happy to see that the new videos, which weren't out the last time I taught this class, and which are made by author Josh Angrist and Marginal Revolution University, add an awesome new layer to this part of the class.
If you are a self-directed learner, and can read and understand these books, you can learn a lot. It's not always possible -- the course covers metrics, as well as coding and research writing -- but the videos coming out make this feasible for more people. There's still incredible value in having all of the resources and support available to you in a class, but I wrote my book with the self-directed learner in mind. In addition, I chose to use the same mathematical notation as in Mastering Metrics so students don't have to learn two different styles, to develop exercises to complement their discussion of key econometric concepts, and most importantly, which enable students to put their knowledge into action by carrying out a research project. The research projects emphasize replication of published research as an intro to research methods, and I discuss how replications can be reanalyzed, extended, and turned into original research.
I'll say more about the other classes in this program in a different post or when I can update this article. We've been able to do some exciting things with the core theory courses which enable us to cover Intermediate Micro and Macro books, cover to cover, with the rigor and transparency of oral exams. But for now I'll just tell you the resources I'm using in the first module of my Fall 2023 metrics class; if you want to follow along at home, feel free to post questions or thoughts to the comments, or send me an email.
ECON 103a: Intro to Econometrics and Research Methods
Data and the American Dream, Appendix A and Glossary
Intro to Class (below) and Intro to Module 1 (coming soon)
Coding Assignment #1 (video, software program, article ... In addition to running the R Studio program in the cloud, students will also down R and R Studio on their computers for use in next week's coding assignment.)
Term Paper Assignment #1 (After completing Coding Assignment #1, select a title for an original research project by filling in the blanks: "Is ___ a good major for future ___ ?" This is a modified version of an article John Winters (2016) titled "Is Economics a Good Major for Future Lawyers", which is the article students in this class will replicate and reanalyze.)
Quiz (TBA: questions from end of MRU videos, and other questions based on Mastering Metrics Ch 1.)
I'm not able to post all the content for the course right now, I will use MRU videos in two more modules, and create videos for each coding assignment, as well as lecture videos on topics including statistical inference and econometric principles, but the core of the course are the readings, and here are all the rest of the readings for the course (Modules 2-8):
Mastering Metrics Appendix to Chapter 1
Mastering Metrics Chapters 2 (including the Appendix and supplemental notes)
Mastering Metrics Chapter 5.
Data and the American Dream, Chapters 1-3.
Thus the whole course covers Core Regression, Randomized Experiments and an Intro to Difference-in-Differences. Students learn to code, to do original analysis with big data sets, and to write up their results in a term paper.
For more example materials, including a syllabus, please see my 2020 blog post (from the last time I taught this course, and from before my book was published), and especially the links at the end, here.
Finally, here are the course learning objectives (CLOs), which form the basis of the questions I ask students during their oral exams:
STUDENTS WILL BE ABLE TO:
(CLO 1) Explain basic methods in econometrics and identify correct procedures:
a) Explain the difference between variables and a statistic in the context of a regression equation.
b) Define the terms "causal effect" and "ideal experiment." Explain the difference between descriptive statistics, inferential statistics, and causal inference.
c) Give two examples of difference-in-means tests using experimental and observational data, and explain when we can and cannot interpret a difference-in-means as an estimate of a causal effect.
d) Describe how to use a simple (bivariate) regression model to carry out a difference in means test.
e) Give an example of a regression coefficient estimate that suffers from omitted variable bias, and explain how the regression control technique could reduce bias in the example. Use the OVB equation to illustrate the two OVB conditions.
f) Describe all the numbers in a regression results table in an economics book or journal article; write the regression equation, identifying the independent and dependent variables; identify the main independent variable of interest; interpret the models, including polynomial and log models; test their statistical significance; evaluate them in terms of any potential bias.
g) Discuss best practices in estimating standard errors.
h) Discuss an example of a natural experiment where:
1) a difference-in-means is a plausibly causal effect, and
where a difference-in-difference (D-in-D) in means is a plausible
causal effect. Finally, explain how an interaction model automates the
estimation of a D-in-D estimate.
(CLO 2) Use technology to analyze data:
a) Create summary statistics for variables in a data set using the R software program.
b) Estimate a regression model (coefficients and standard errors) and create a scatter plot with a regression line in R.
c) Download data from the Internet and read it into a statistical software package
d) Run an R script associated with a published research study by modifying the directory path, installing required packages, loading data, and obtaining results.
e) Create a new script by modifying an existing script, and use your original results in a term paper
(CLO 3) Prepare a scholarly research paper describing an original regression analysis:
a) Formulate an interesting and important research question.
b) Locate and describe data from the Internet or other sources.
c) Search and analyze scholarly literature related to the research question.
d) Write a review of econometric literature that is integrated and not merely an annotated bibliography; list and describe relevant studies and their research questions, the data and methods they used, and the results they found. Highlight any studies that provide compelling estimates of well-defined causal effects, or explain why a study does not.
e) Develop, estimate, and interpret a statistical model that can be used with the data to answer a question that is original and contributes to the literature.