Instituto Tecnológico Autónomo de México (ITAM)
Professor: Mauricio Romero
- E-mail: mtromero@itam.mx
- Syllabus
Slides
- 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
- Lecture 1
- Lecture 2
- Lecture 3
- Lecture 4
- Lecture 5
- Lecture 6
- Lecture 7
- Lecture 8
- Lecture 9
- Lecture 12
LLM Para la clase
- Se ha desarrollado un modelo de lenguaje (LLM) específicamente para “Inferencia Causal/Microeconometria”, entrenado con diapositivas, clases grabadas y exámenes anteriores. Será la primera vez que lo usemos; por ello, úsenlo con responsabilidad: su tasa de alucinaciones es menor que la de otros modelos, pero no es infalible. Una ventaja clave: puede señalar los conceptos que aún generan confusión entre ustedes, y con esa información puedo enfocarme en revisarlos durante la clase.
- https://app.alldayta.com/instituto-tecnologico-autonomo-de-mexico/inferencia-causal
Project/Homework
Páginas de cursos relacionados con ejercicios, soluciones, y otras cosas
- Microeconometría Aplicada (ITAM, 2023-2)
- Microeconometría Aplicada (ITAM, 2023-1)
- Microeconometría Aplicada (ITAM, 2021-1) – Clases grabadas
- Microeconometría Aplicada (ITAM, 2020-2) – Clases grabadas
- Experimental Methods in Development Research (University of Bern, 2020)
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
- Nick Huntington-Klein’s The Effect: An Introduction to Research Design and Causality
- 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
- Asjad Naqvi’s repository with resources for difference-in-differences
- Andrew Goodman-Bacon and Pedro Sant’Anna: “New Developments in Difference-in-differences Estimators” at the Electronic Health Economics Colloquium on June 16, 2021
- Kyle Butts’s visual guide to Goodman-Bacon decomposition of TWFE
- Gary Chamberlain’s lecture notes (posted by Paul Goldsmith-Pinkham)
- Paul Goldsmith-Pinkham’s Applied Empirical Methods PhD Class
- Sarah Miller’s cheat sheets for RCTs, RDD, and DiD
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
- Grant McDermott’s Efficient Simularion in R post
- Ljubica “LJ” Ristovska’s Language-Agnostic Guide to Programming for Economists
- TablesGenerator, to convert Excel tables to LaTeX
- DIME (at the World Bank) R-training course
- Monica Alexander workshop on social media data for population researchers
Stata resources
- IPA’s Stata training
- MITx: J-PAL-101: Evaluating Social Programs
- J-PAL data cleaning in Stata
- UCLA’s Stata resources
- Stata introduction by German Rodriguez
- Stata introduction by Oscar Torres-Reyna
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 research resouces (lots of Stata examples)
- J-PAL’s data visualization resources
- Alex Coppock’s note on visualizations for RCTs
- Layout Parser: an open-source deep-learning powered library, that provides a variety of tools for automatically processing document image data at scale.
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”