Teaching

If you want to ask me to become a research mentor, please read this guideline document and be prepared.

On this page, I will publish my teaching materials for undergraduate and Ph.D courses.

Lectures

Economics of Race, Gender, and Culture, Undergraduate, JHU, Fall 2023

Syllabus     Example Codes  PAST Problem Set 

Structural Approach in Family and Cultural Economics, Ph.D 2nd year, JHU, Fall 2023

Syllabus     Problem Set

Introduction to Structural Estimation, Ph.D module, Unige, Fall 2019

Syllabus

Students are encouraged to learn one high-level programming language (R, Python, Matlab, Julia) and one low-level programming language (C/C++, Fortran), and parallelization techniques. The following online materials may be useful to learn coding.

R/Rcpp

Coursera (JHU)

EconR 

Seamless R and C++ 

Writing an R package

Rpackage1     Rpackage2     Rpackage3    Rpackage4   Rpackage5

R/Matlab

R-Matlab Reference 

R/Python

Datacamp 

Python / Julia

QuantEcon (Dynamic Programming) 

Python

Econ-Ark (Dynamic Programming) 

Using Python to Access Web Data (Web Scraping) 

Applied Text Mining in Python (Natural Language Processing) 

C++

Udemy 

Udacity 

STATA

Writing an Estimation Command

Parallelization

Fundamentals of Parallelism 

Latex

Beamer Tips       Example Codes

Latex Introduction

Coding Tips

Gentzkow and Shapiro

Stata Coding Tips

Version Control

Git

Useful R packages

Converting Data File Formats

MPEC