Instituto Tecnológico Autónomo de México (ITAM)
Professor: Mauricio Romero
- E-mail: mtromero@itam.mx
- Syllabus
Slides
- Lecture 0 – Admin – native LaTeX file
- Lecture 1 – Intro to R – native LaTeX file
- Lecture 2 – Working with data in R – native LaTeX file
- Lecture 3 – Basics of programming – native LaTeX file
- Lecture 4 – Stats review – native LaTeX file
- Lecture 5 – Summarizing data – native LaTeX file
- Lecture 6 – Potential outcomes and the experimental ideal – native LaTeX file – Slides with notes
- Lecture 7 – OLS review – native LaTeX file – Slides with notes
- Lecture 8 – Beyond the basics of OLS– native LaTeX file – Slides with notes
- Lecture 9 – Panel Data– native LaTeX file – Slides with notes
- Lecture 10 – Difference-in-Differences – native LaTeX file– Slides with notes
- Lecture 11 – Instrumental Variables – native LaTeX file – Slides with notes
- Lecture 12 – Regression Discontinuity – native LaTeX file– Slides with notes
R Code
Project/Homework
- Taller 1
- Examen 1
- Taller 2
- Taller preparación para el Examen 2 (no hay que entregarlo)
- Examen 2
- Taller 3 —Bosch y Campos-Vázquez (2014)
- Taller preparación para el Examen 3 (no hay que entregarlo)
- Examen 3
Recordings
- 06/08/2020 – Lecture 0 + most of lecture 1
- 11/08/2020 – End of lecture 1 + 50% of lecture 2
- 13/08/2020 – Next 50% of lecture 2
- 18/08/2020 – 40% of lecture 3
- 20/08/2020 – Next 60% of lecture 3
- 25/08/2020 – 50% of lecture 4
- 27/08/2020 – Next 50% of lecture 4
- 01/09/2020 – 50% of lecture 5
- 03/09/2020 – Next 50% of lecture 5
- 08/09/2020 – 33% of lecture 6
- 15/09/2020 – Next 33% of lecture 6
- 17/09/2020 – Last 33% of lecture 6 + 10% of lecture 7
- 22/09/2020 – Next 20% of lecture 7
- 29/09/2020 – Next 20% of lecture 7
- 01/10/2020 – Next 20% of lecture 7
- 06/10/2020 – Next 20% of lecture 7
- 08/10/2020 – Next 10% of lecture 7 + first 20% of lecture 8
- 13/10/2020 – Next 30% of lecture 8
- 15/10/2020 – Next 30% of lecture 8
- 20/10/2020 – Final 20% of lecture 8 + review for exam 2
- 27/10/2020 – 90% of lecture 9
- 29/10/2020 – Last 10% of lecture 9 + 20% of Lecture 10
- 05/11/2020 – Last 80% of lecture 10
- 10/11/2020 – First 30% of lecture 11
- 12/11/2020 – Next 30% of lecture 11
- 17/11/2020 – Next 30% of lecture 11
- 19/11/2020 – Final 10% of lecture 11 + first 10% of lecture 12
- 24/11/2020 – Next 50% of lecture 12
- 26/11/2020 – Final 40% of lecture 12
Other resources to learn econometrics
- Seeing Theory – A visual introduction to probability and statistics
- Nick Huntington-Klein’s class, animated plots, econometric/R resources, and Github repository
- Scott Cunningham’s Causal Inference: The Mixtape. A free textbook focusing on causal inference. Github repository
- Mostly Harmless Econometrics (MHE) website
- Pamela Jakiela and Owen Ozier’s Empirical Microeconomics class
- Grant McDermott’s Data Science for Economist class and Github repository
- SciencePo’s “Introduction to econometrics with R” and their Github repository
- A great compendium of easy-to-read papers on econometrics: https://www.aeaweb.org/journals/jep/classroom/econometrics
- EGAP’s methods guide (contains sample R code)
- J-PAL’s research resources and teaching resources
- The World Bank’s methods blog posts (and an updated version from 2020)
- NBER Summer Institute 2007 Methods Lectures
- Common statistical tests are linear models
R and programming resources
- R-Project.org and RStudio.com First install the latest version of R from R-Project. Then, install RStudio
- Some useful R guides and tutorials: swirl, Base R cheat sheet, R-bloggers, and R Studio’s resources, Kelsey Moty’s R Workshops at NYU CDSC Lab
- Jesse M. Shapiro and Matthew Gentzkow’s Code and Data for the Social Sciences: A Practitioner’s Guide
- Grant McDermott’s Data Science for Economist class
- Ljubica “LJ” Ristovska’s Language-Agnostic Guide to Programming for Economists
- TablesGenerator, to convert Excel tables to LaTeX
Other useful resources
- Jesse M. Shapiro’s How to Give an Applied Micro Talk
- Rachael Meager’s Public Speaking for Academic Economists
- Kieran Healy’s Visualization: A Practical Introduction
- Jonathan A. Schwabish’s An Economist’s Guide to Visualizing Data
- A color scheme I like: http://www.mulinblog.com/a-color-palette-optimized-for-data-visualization/
- A couple of blogs on data visualization I like: Blog 1, Blog 2
- J-PAL’s data visualization resources
- Alex Coppock’s note on visualizations for RCTs
Visual tour of OLS
- Ryan Safner’s shiny app
- Seeing theory interactive plot
- SETOSA interactive plots
- Manny Gimond’s intro guide to OLS in R
- SciencePo’s Shinny app
Assorted links
- Ayotzinapa documentary (on Netflix): https://www.netflix.com/title/81013509
- One of my favorite essays (it’s really a speech): http://agaviria.blogspot.com/2016/02/una-ultima-leccion.html
- Two visualizations of inequality: “Where are you in the income distribution?” and “Wealth shown to scale”