This Course at MIT

This Course at MIT pages provide context for how the course materials published on OCW were used at MIT. They are part of the OCW Educator initiative, which seeks to enhance the value of OCW for educators.

Course Overview

This page focuses on the course 14.384 Time Series Analysis as it was taught by Prof. Anna Mikusheva in Fall 2013.

The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. The empirical applications in the course will be drawn primarily from macroeconomics.

This course is part of a sequence of courses on statistics and econometrics in the Economics Department.

Course Outcomes

Course Goals for Students

  • The main objective of this course is to develop the skills needed to do empirical research in fields operating with time series data sets.
  • The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data.
  • Special attention will be placed on limitations and pitfalls of different methods and their potential fixes.
  • The course will also emphasize recent developments in Time Series Analysis and will present some open questions and areas of ongoing research.
 

Curriculum Information

Prerequisites

14.382 Econometrics

Requirements Satisfied

  • H-Level graduate credit
  • 14.384 can be applied toward a Ph.D. in Economics, but is not required.

Offered

Every fall semester

The Classroom

  • A photograph taken from the back of a classroom. The room has three rows of seating and blackboards at the front of the room.

    Lecture

    Lectures for the course were held in this room, which has a capacity of 50 students.

  • A photograph taken from the front of a classroom. The room contains several rows of chairs with desk arms.

    Recitation

    This is the classroom where the teaching assistant held recitation sessions.

 

Assessment

The students' grades were based on the following activities:

The color used on the preceding chart which represents the percentage of the total grade contributed by problem sets. 60% Five problem sets
The color used on the preceding chart which represents the percentage of the total grade contributed by the final exam. 40% Take-home final exam
 

Student Information

On average, 32 students take this course each time it is offered.

Breakdown by Major

This course is an advanced topic class intended for Ph.D. students in economics and finance. It is also popular among students pursuing a Ph.D. in engineering.

 

How Student Time Was Spent

During an average week, students were expected to spend 10.5 hours on the course, roughly divided as follows:

Lecture

3 hours per week

Met 2 times per week for 1.5 hours per session, for 26 sessions.

 

Recitation

1.5 hours per week
  • Met 1 time per week for 1.5 hours per session, for 13 sessions.
  • Led by a teaching assistant, the recitation sessions covered topics covered in the week’s lectures and included help with problem sets.
 

Out of Class

6 hours per week

Students worked on five problems sets and a final exam taken at home.

 

Semester Breakdown

WEEK M T W Th F
1 No classes throughout MIT. No classes throughout MIT. No session scheduled. Lecture session. Recitation session.
2 No session scheduled. Lecture session. No session scheduled. Lecture session. Recitation session.
3 No session scheduled. Lecture session. No session scheduled. Lecture session. No classes throughout MIT.
4 No session scheduled. Lecture session. No session scheduled. Lecture session; assignment due date. Recitation session.
5 No session scheduled. Lecture session. No session scheduled. Lecture session. Recitation session.
6 No session scheduled. Lecture session. No session scheduled. Lecture session. Recitation session.
7 No classes throughout MIT. No classes throughout MIT. No session scheduled. Lecture session; assignment due date. Recitation session.
8 No session scheduled. Lecture session. No session scheduled. Lecture session. Recitation session.
9 No session scheduled. Lecture session. No session scheduled. Lecture session. Recitation session.
10 No session scheduled. Lecture session. No session scheduled. Lecture session; assignment due date. Recitation session.
11 No classes throughout MIT. Lecture session. No session scheduled. Lecture session. Recitation session.
12 No session scheduled. Lecture session. No session scheduled. Lecture session. Recitation session.
13 No session scheduled. Lecture session. Assignment due date. No classes throughout MIT. No classes throughout MIT.
14 No session scheduled. Lecture session. No session scheduled. Lecture session; assignment due date. Recitation session.
15 No session scheduled. Lecture session. No session scheduled. 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; take-home exam due.
Displays the color and pattern used on the preceding table to indicate dates when classes are not held at MIT. No classes throughout MIT
Displays the color used on the preceding table to indicate dates when lecture sessions are held. Lecture session
Displays the symbol used on the preceding table to indicate dates when assignments are due. Assignment due date
Displays the color used on the preceding table to indicate dates when no class session is scheduled. No class session scheduled
Displays the color used on the preceding table to indicate dates when recitation sessions are held. Recitation
Displays the symbol used on the preceding table to indicate dates when a take-home exam is due. Exam
 

Instructor Insights

Assessment

Prof. Mikusheva strongly believes that the best way to learn the techniques of the course is by doing. Every problem set will include an applied task that may include computer programming. She does not restrict the students in their choice of computer language. The professor also does not require her students to write all programs by themselves from scratch. They may use user-written parts of codes found on the Internet, but the instructor does require that students understand the program used and properly document it with all needed citations of original sources. Collaboration with other students on problem sets is encouraged, however, the problem sets should be written independently.