"Computational Methods for Economists using Python", by Richard W. Evans. Tutorials and executable code in Python for the most commonly used computational methods in economics.

Collection of published papers that estimate dynamic programming models

Python and Julia Code for Structural Behavioral Economics

promotional material for our work on Eckstein-Keane-Wolpin models

Code to solve exercises from Adda and Cooper's "Dynamic Economics" book

Implementing the BLP method for random effects choice model using Julia.

An applied-micro tutorial using Python on Jupyter notebook showing how to perform a structural estimation excercise using finite dependence and bootstrapping.

A Python version of Miranda and Fackler's CompEcon toolbox

Topics in Advanced Econometrics (ResEcon 703). University of Massachusetts Amherst. Taught by Matt Woerman

Intended to implement multiple-indicator, multiple-cause (MIMIC) modelling with discrete indicators in R.

Econometrics lecture notes with examples using the Julia language

Replication of Networks in Conflict Econometrica Paper

Simulation and estimation of a simple job search model for structural econometrics study group

Labor Mobility with Environmental Regulation

Structural Applied Micro Graduate Course Problem Set

MACS 40200 (Winter 2020): Structural Estimation

Collection of published papers that estimate dynamic programming models

Teaching material for Lectures in Dynamic Programming

Teaching materials from DSE2019 summer school at Chicago Booth