## Syllabus

[**Fall 2018**] | [Fall 2017] | [Fall 2016]

Course Content on Github, see this post for more.

There are three kinds of lies: lies, damned lies, and statistics. - Benjamin Disraeli

Econometrics is the application of statistical tools to quantify and measure economic relationships in the real world. It uses real data to test economic hypotheses, quantitatively estimate causal relationships between economic variables, and to make forecasts of future events. The primary tool that economists use for empirical analysis is ordinary least squares (OLS) linear regression, so the majority of this course will focus on understanding, applying, and extending OLS regressions.

I have three goals for everyone taking this course:

(1) to understand and evaluate statistical and empirical claims

(2) to understand research design and hypothesis testing

(3) to gain experience working with, interpreting, and communicating real data. I am less concerned with forcing you to memorize and recite proofs of statistical estimator properties, and more concerned with the development of your intuitions and the ability to think critically as an empirical social scientist - although this will require you to demonstrate proficiency with some intermediate statistical and mathematical tools.

To these ends, in addition to lectures about the estimation methods, you will read several journal articles with an eye to understanding and appraising their empirical claims, use *R*—a leading professional software package—to complete problem sets using data, and write a brief empirical paper using data. By the end, you should feel comfortable working with economic data and understanding the empirical claims of others. *R* is an extremely powerful open source statistical software package that is used by economists, statisticians, and data scientists. It is very valuable and much of this course is geared towards training you how to use and apply it. The best training, of course, is for you to simply learn by doing.

## Resources

I have put together some short guides about using `R`

, `R Markdown`

, and `R Projects`

for data analysis, writing papers and presentations, and organizing workflow in a reproducible and efficient way. Some of these are hosted on this website, and others are code on GitHub. All lecture files above, along with other resources for this course, are available on GitHub for anyone to inspect the source code (in `R Markdown`

`.Rmd`

files), as described in my post.