John Eric Humphries

Teaching

This page provides course material for courses taught prior to joining Yale. My CV contains details on my teaching at Yale.

ECON 21410: Computational Methods for Economics (2014)

This course introduces the empirical and computational techniques necessary for numerical estimation and simulation in economics. Through examples in economics, the course covers topics such as optimization, function approximation, and monte carlo techniques. Emphasis will be placed on developing effective programming and research practices. The course is structured through a series of applications in topics such as segregation, occupational choice, and repeated games. The course will be taught in R. Though helpful, no previous experience with R is required.

Class Syllabus

Here

Problem Sets

Problem Set 1, (solution) , (solution code)

Problem Set 2, (code for pset2), (solution) , (solution code)

Problem Set 3, (solution) , (solution code)

Problem Set 4, (solution) , (solution code)

Problem Set 5, (partial solution) , (partial solution code)

Problem Set 6, (data) , (partial solution) , (partial solution code)

Problem Set 7, (partial solution) , (partial solution code)

Class Examples

Maximization Example Code from First Class: 1 , 2 , 3

Example knitr file that calls outside code: example.Rnw , example.R , example.pdf

Example Schelling Segregation Model: example_schelling.R

Example of Sorting on Property Price: econ21410_example_landpricemodel.R

Examples of using apply functions: apply_examples.R

Selection Class Notes

Regression and Function Approximation Code: econ21410RegressionExamples.R

Example Ordered Probit Model: econ21410ProbitMLE.R

Integration: econ21410ExampleIntegration.R

Time Series: econ21410ARMAexample.R

Selection with Random Coefficient: Example.R , Example_v2.R , Example_v2.cpp

Additional Course Files

Software Overview

Basic Operations in R

These files are also on our github page here.

ECON 21410: Computational Methods for Economics (2015)

Class Syllabus

Here

Problem Sets

Problem Set 1, (solution), (code)

Problem Set 2, (code for pset2), (solution), (code)

Problem Set 3, (solution), (code)

Problem Set 4

Problem Set 5, (solution), (code)

Problem Set 6, (data)

Problem Set 7

Class Examples

Example knitr file that calls outside code: example.Rnw , example.R , example.pdf

Example Schelling Segregation Model: example_schelling.R

Examples of nleqslv, optimize, and optim: example_nleqslv.R

Examples of regression, data summary, and function approximation: example_workingWithData.R

Examples of Maximum Likelihood Estimation: example_mle.R

Examples of Numerical Integration: example_integration.R

Examples of Apply: example_apply_commands.R

Examples of dplyr: example_dplyr.R

Examples of a random coefficients model (includes parallel and cpp implementation): example_randomCoefficients.R , example_randomCoefficients.cpp ,

Additional Course Files

Software Overview

Basic Operations in R

Segregation slides from class

Housing slides from class

Additional files and help are also on our github page here.

Econ 61800: Practical Computing for Economists (2014)

This colloquium covers the computer tools and programming techniques to implement and test economic ideas and theories quantitatively. It is more of an “engineering” than a “theory” course, focusing on the practical – working on problems and solutions. The goal is not only to provide students with an introduction to two valuable programming languages (R and C++) but also to introduce good programming, data, and project management techniques.

The class syllabus can be downloaded Here

Class Examples

Rapid Intro To R: code

Working with Data in R: code

Writing Programs in R: code

MCMC Factor Analysis in R: code

Econ 61800: Practical Computing for Economists (2015)

Class Examples

An Applied Introduction to R: code

Working with Functions in R: code

Econ 61800: Practical Computing for Economists (2016)

This colloquium covers the computer tools and programming techniques to implement and test economic ideas and theories quantitatively. It is more of an “engineering” than a “theory” course, focusing on the practical – working on problems and solutions. The goal is not only to provide students with an introduction to a number of valuable programming languages, but also to introduce good programming, data, and project management techniques.

Class Examples

Day 1: code

Day 2: code

Day 3: R , faster R , C++ for faster R , R and C++ basics , R and Armadillo basics

Economic REU: Microeconomics with R (2014 - 2017)

This will be an interactive session covering R basics and standard programming techniques in R. Students should bring their computers to participate if possible.Comparisons will be drawn between R, Stata, and Matlab. Using the tools covered in the previous session in the second session will live-code a simulation of Schelling's Segregation model. This will cover control flow, random number generation, matrix manipulation, functions, proper coding etiquette, and plotting in R.

Please install R and Rstudio and run this code prior to arrival (code).

Files from Past Years (including Stata intro): Stata code | Stata data | CPI data | R code