(Trying out a different syllabus format)
Andrew Pua
My name is Andrew Pua.
I usually teach difficult courses to heterogeneous audiences, so it is hard to perfect the material.
I expect students to push themselves out of their comfort zone.
Cosma Shalizi makes the following arguments against video recordings:
“… the value of class meetings lies precisely in your chance to ask questions, discuss, and generally interact. (Otherwise, you could just read a book.)”
“They tempt you to skip class and/or to zone out and/or try to multi-task during it. (Nobody, not even you, is really any good at multi-tasking.) Even if you do watch the recording later, you will not learn as much from it as if you had attended in the first place.”
Slides are usually released a week or so ahead of time.
Email me at andrewypua@outlook.com.
Starting from September 29, Microsoft Teams office hours are every Thursday and Friday from 1400 to 1430 Bari local time. Bring lunch/dinner/snacks if you want.
One of your classmates reached out to me about R installation and an exercise in fasteR.
What they did was to reach out by email, attach clear screenshots of the problem and provide background for the problem.
Even more important is the sincere attempt to address their problem locally first, Googling stuff and tracking similar issues. They also reported their experience and their own processing of these aspects.
The problems were:
I am sharing these with you in case you have encountered similar problems.
As a result, communication was smoother and my response was faster (no pun intended)!
Questions related to the course are definitely encouraged, but …
Asking questions in our Teams channel ECONOMETRICS (rather than privately) is for the benefit of everyone.
Asking questions publicly is meant to improve communication skills in a virtual environment and encourage cooperation amongst yourselves.
Screenshots can help, but take time to think about how to formulate and write your question.
They call this course applied econometrics.
From Mastering ’Metrics book flap: “Applied econometrics, known to aficionados, as ’metrics, is the original data science. ’Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs.”
From the introduction: “Economists’ use of data to answer cause-and-effect questions constitutes the field of applied econometrics, … The tools of the ’metrics trade are disciplined data analysis, paired with the machinery of statistical inference.”
From Maddala (2013): Econometrics is “the application of statistical and mathematical methods to the analysis of economic data, with the purpose of giving empirical content to economic theories and verifying them or refuting them.”
Based on coordination with the instructors of Applied Econometrics 2, I was broadly tasked to cover linear regressions and instrumental variables.
Since I am handling a heterogeneous audience, I need to cover the most important parts of probability theory and statistical inference, which are usually prerequisites of an econometrics course.
But the focus and sequencing are different from the standard approaches.
I emphasize:
Understand conceptually and intuitively core econometric methods with a focus on whether they can be applied to a particular empirical context
Be comfortable enough with the common mathematical and statistical thinking driving these methods in order to apply, build, and extend them
Be able to move from a theoretical question or a theoretical model to a relevant econometric model and the econometric method to be used
Be able to interpret the results and not just be able to push buttons using econometric software
Be prepared enough for Applied Econometrics 2 and your future research projects
Everything in probability and statistics until regression:
Dekking, Kraaikamp, Louphaä, and Meester, A Modern Introduction to Probability and Statistics. Springer-Verlag, 2005.
Wasserman, All of Statistics: A Concise Course in Statistical Inference. Springer-Verlag, 2004.
Aronow and Miller, Foundations of Agnostic Statistics. Cambridge University Press, 2019.
More or less standard textbooks:
Sticking to a particular flavor:
Huntington-Klein, The Effect: An Introduction to Research Design and Causality. CRC Press, 2021.
Cunningham, Causal Inference: The Mixtape. Yale University Press, 2021.
Angrist and Pischke, Mastering ’Metrics. Princeton University Press, 2014.
Angrist and Pischke, Mostly Harmless Econometrics. Princeton University Press, 2009.
Odd men out:
On the surface, maybe.
In my experience, applied econometrics may be harder than econometric theory in three senses:
In the words of Angrist and Pischke (2017), “Econometrics at its best is distinguished from other data sciences by clear causal thinking.”
A counterpoint is from Diebold’s blog entry: There is another “half of econometrics” which emphasizes careful modeling.
Bottom line: Applied econometrics has both elements!
The final course grade is based on
The passing mark for the course is 60%. I use a 0-100 scale.
To make you aware of your mental mindset (see Farrington (2013)):
To give you a chance to look inward
Submit during the final exam date in a reproducible format.
Details about this format will be made available as we progress through the semester, as you will be using R and R Markdown (or Quarto) along the way.
The grading of the individual paper does not depend only on the final output, but it also depends on the milestones which are to be set as we progress through the term.
Milestones include but are not limited to:
Milestones are meant to spread accountability over the entire term, to keep you on your toes, and for you to have something concrete and real at the back of your mind as we progress through the course.
The final exam is set up as follows:
It is tentatively scheduled on January 2023.
You are expected to know how to read R code, program in R, and understand R output based on the topics discussed.
You are also expected to know details about your individual paper.
There are rules and regulations which set boundaries on what members of the academic and research community could do.
EGEI and IGP have their own rules and you should spend time being aware of them.
Why should you care about academic and research integrity?
I would argue beside these rules and regulations as follows: