#! Econ 2713A: Syllabus
# Course Information
Douglas Hanley, [firstname.lastname@example.org](mailto:email@example.com)
Lecture: MW 10:30-11:45, 4940 Posvar
Office hours: TBD, 450X Posvar
In this half-semester course, we're going to learn how to do various things with computers. These skill will, I suspect, prove useful as you progress through graduate school and beyond. We'll cover both techniques for computing the equilibria of complex economic models and advanced data analysis methods. We'll also cover more cutting edge topics such as machine learning and working with Big Data™.
You should have a computer of some sort. If that's an issue, let me know and I can loan you a laptop. We'll be doing everything in Python. The easiest way to get this is through the Anacond distribution (https://www.anaconda.com/download). Advanced users of OSX or Linux can also install Python natively through a package manager.
For homework writeups, you'll want to use LaTeX or some equivalent thereof to generate PDFs. If you don't already know how to do this, now is the time to learn! You'll only be using it more from now on. I'm also working on a LaTeX replacement (https://github.com/iamlemec/elltwo) you could try out, though its not for the faint of heart quite yet.
There is no textbook, but here are some useful online resources:
[Data Science](https://github.com/iamlemec/data_science) tutorial by Yours Truly. Some but not all of this will be covered early on in class.
[Modern Pandas](https://tomaugspurger.github.io/modern-1-intro.html) tutorial by Tom Aguspurger. This gives a hands on overview of various advanced techniques one can employ using pandas.
[Tensorflow Tutorial](https://www.tensorflow.org/tutorials/estimators/linear) by Google. Use machine learning techniques to analyze and predict US Census data.
[Economics Tools](https://github.com/iamlemec/mectools) by Yours Truly. Some useful tools for economics related tasks. We may build on this over the course of the term.
To print the slides you need to let them know you'll be printing them. To do so, add "&print-pdf" to the end of the URL (but before the "#" if there is one). For instance, to print lecture 1, go to:
After that you can choose print or print to PDF from your web browser and choose *landscape* mode.
There will be weekly homework assignments. You are encouraged to work together on these, but you must write and fully understand your own work individually. These will consist mostly of coding and data analysis but will also involve some theoretical derivations.
# Course Outline
This is an approximate timeline for the course. Actual results may vary.
| Session | Topic |
| ------- | ------------------------------------ |
| 0 | The Ecosystem |
| 1 | Continuous Time Models |
| 2 | Distributions and Transitions |
| 3 | Estimation: ML and GMM |
| 4 | Data Analysis with Pandas |
| 6 | Machine Learning |
| 7 | Bayesian Estimation |
| 8 | Homotopy Methods |
Try to complete your assignments on time. If you need an extension for a plausible reason, let me know and we can work something out. You are free to consult any sources in the course of completing your homework. All I ask is that your properly attribute them.
Statement on academic integrity:
Cheating/plagiarism will not be tolerated. Students suspected of violating the University of Pittsburgh Policy on Academic Integrity, from the February 1974 Senate Committee on Tenure and Academic Freedom reported to the Senate Council, will be required to participate in the outlined procedural process as initiated by the instructor. A minimum sanction of a zero score for the quiz or exam will be imposed.