This Course at MIT

Course Overview

This page focuses on the course 14.387 Applied Econometrics: Mostly Harmless Big Data as it was taught by Prof. Joshua Angrist and Prof. Victor Chernozhukov in Fall 2014.

This course covers empirical strategies for applied micro research questions. Topics include:

  • regression and matching
  • instrumental variables
  • differences-in-differences
  • regression discontinuity designs
  • standard errors
  • analysis of high-dimensional data sets a.k.a. “Big Data”

Course Outcomes

Course Goals for Students

  • Understand empirical strategies for applied micro research questions
  • Engage in discussion about these strategies
  • Apply understanding in solving problems 

 

Possibilities for Further Study/Careers

Mostly my audience is headed for careers in research - either in academia, government, or NGOs. There’s also a growing private-sector contingent, people headed for jobs at places like Google, Microsoft Research, and Amazon.

 

Instructor Insights

In the column, “Mastering Metrics: Teaching Ecnomometrics,” Prof. Angrist and Jorn-Steffen Pischke wrote about the need to "overhaul" econometrics pedagogy. Below, Prof. Angrist shares aspects of his pedagogical practice in 14.387 Applied Econometric: Mostly Harmless Big Data. 

We try to keep the content real by using authentic empirical questions that can be answered with real data. We think logically, not abstractly.

14.387 Applied Econometrics: Mostly Harmless Big Data is a graduate-level course. Although many students come wanting to just "sit in," all students are given name cards and required to participate in discussions. We also give long challenging problems sets - good performers earn prizes and recognition by completing these problems.

 

Curriculum Information

Prerequisites

14.382 Econometrics I

Requirements Satisfied

None

Offered

This course is usually taught once aew year. Up until 2014, it was taught in the spring. However, it is now taught in the fall.

The Classroom

  • Classroom with tiered seating and sliding chalkboards.

    Lecture

    Lectures took place in a room with capacity for 70 students, sliding chalkboards, LCD video projector and fixed modern tables with tiered seating.

 

Assessment

The course features three graded problem sets, which had to be submitted on time to be graded for credit. The course is graded pass/fail.

Student Information

37 students took this class when it was offered in Fall 2014.

Typical Student Background

Most students were from Ph.D. or professional schools (like the Harvard Kennedy School). We typically get some precocious research assistants. Most of JPAL's (Abdul Latif Jameel Poverty Action Lab) local full-timers seem now to take the course. SEII (School of Effectiveness & Inequality Initiative) full-timers also take the course. 

Many students have read at least some of the Angrist and Pischke book(s). These provide a good screen. If you like that stuff, and find it accessible and relevant, you'll like the course.

 

How Student Time Was Spent

During an average week, students were expected to spend 12 hours on the course, roughly divided as follows:

In Class/Lecture

3 hours per week

Met 2 times per week for 1.5 hours per session.

In addition, several recitation sessions were held throughout the year; these were facilitated by a teaching assistant.

The course was co-taught by two instructors, Prof. Josh Angrist taught topics I-VII and Prof. Victor Chernozhukov started teaching from topic VIII.

 

Out of Class

9 hours per week

Students spent time outside of class working on three graded problem sets as well as the readings.

 

Semester Breakdown

WEEK M T W Th F
1 No classes throughout MIT. No classes throughout MIT. No session scheduled. Lecture session scheduled. No session scheduled.
2 No session scheduled. Lecture session scheduled. No session scheduled. Lecture session scheduled. Recitation session scheduled.
3 No session scheduled. Lecture session scheduled. No session scheduled. Lecture session scheduled. No classes throughout MIT.
4 No session scheduled. Lecture session scheduled. No session scheduled. Lecture session scheduled. Recitation session scheduled.
5 No session scheduled. Lecture session scheduled. No session scheduled. Lecture session scheduled. No session scheduled.
6 No session scheduled. Lecture session scheduled; assignment due date. No session scheduled. Lecture session scheduled. No session scheduled.
7 No classes throughout MIT. Lecture session scheduled. No session scheduled. Lecture session scheduled. No session scheduled.
8 No session scheduled. Lecture session scheduled. No session scheduled. Lecture session scheduled. Recitation session scheduled.
9 No session scheduled. Lecture session scheduled. No session scheduled. Lecture session scheduled. No session scheduled.
10 No session scheduled. Lecture session scheduled. No session scheduled. Lecture session scheduled; assignment due date. No session scheduled.
11 No classes throughout MIT. No classes throughout MIT. No session scheduled. Lecture session scheduled. No session scheduled.
12 No session scheduled. Lecture session scheduled. No session scheduled. Lecture session scheduled. Recitation session scheduled.
13 No session scheduled. Lecture session scheduled. No session scheduled. No classes throughout MIT. No classes throughout MIT.
14 No session scheduled. Lecture session scheduled. No session scheduled. Lecture session scheduled. No session scheduled.
15 No session scheduled. Lecture session scheduled. No session scheduled; assignment due date. No classes throughout MIT. No classes throughout MIT.
16 No classes throughout MIT. No classes throughout MIT. No classes throughout MIT. No classes throughout MIT. No classes throughout MIT.
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