Lecture Notes · Lecture Notes
MIT OpenCourseWare
Time Series Analysis
- Instructor
- Prof. Anna Mikusheva
- Term
- Fall 2013
- Level
- Graduate
- What's inside
- 1 exam · 24 lecture notes · 1 reading · 14 other files · 5 web pages
- Materials open
- MIT OCW
About this course
Source: MIT OpenCourseWare 14.384 Time Series Analysis, Fall 2013, Prof. Anna Mikusheva — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.
Materials are grouped the way MIT organized them. A verified, course-wide order has not been established here.
Before you start
Prerequisites are not listed here. Check MIT's own syllabus for this course.
How this course was organized
What's here, and what isn't
- MIT's own materials here did not support a verified, course-wide order, so they are grouped by section instead.
Not included here
107 items from the source could not be safely included:
- MIT's page format for this item wasn't recognized clearly enough to place it safely. (107 items)
Materials by section
Grouped by section, as MIT organized them. Not sequenced or ordered.
Lecture Notes
23 files
- Lecture Notes 1: Stationarity, Lag Operator, ARMA, and Covariance Structure Download
- Lecture Notes 2: Limit Theorems, OLS, and HAC Download
- Lecture Notes 3: More HAC and Intro to Spectrum Download
- Lecture Notes 4: Spectrum Download
- Lecture Notes 5: Spectrum Estimation and Information Criteria Download
- Lecture Notes 6: GMM Download
- Lecture Notes 7-8: Weak IV Download
- Lecture Notes 9: Bootstrap Download
- Lecture Notes 10: Introduction to VARs Download
- Lecture Notes 11: VARs Download
- Lecture Notes 12-13: Structural VARs Download
- Lecture Notes 14: Factor Models Download
- Lecture Notes 15: Factor Models Part 2 Download
- Lecture Notes 16: Empirical Processes Download
- Lecture Notes 17: Unit Roots Download
- Lecture Notes 18: More Non-Stationarity Download
- Lecture Notes 19: Breaks and Cointegration Download
- Lecture Notes 20: Cointegration Download
- Lecture Notes 21: Filtering, State space models, Kalman filter Download
- Lecture Notes 22: State-space models, ML estimation, DSGE models Download
- Lecture Notes 23-24: Intro to Bayes approach, Reasons to be Bayesian Download
- Lecture Notes 25: MCMC: Metropolis Hastings Algorithm Download
- Lecture Notes 26: MCMC: Gibbs Sampling Download
Assignments and Exams
Recitations
13 files
Other materials from this course
Course outline
Lecture Notes · Lecture Notes
Lecture 2: Limit theorems, OLS, and HAC
Lecture Notes · Lecture Notes
Lecture 3: More HAC and intro to spectrum
Lecture Notes · Lecture Notes
Lecture 4: Spectrum
Lecture Notes · Lecture Notes
Lecture 5: Spectrum estimation and information criteria
Lecture Notes · Lecture Notes
Lecture 6: GMM
Lecture Notes · Lecture Notes
Lecture 9: Bootstrap
Lecture Notes · Lecture Notes
Lecture 10: Introduction to VARs
Lecture Notes · Lecture Notes
Lecture 11: VARs
Lecture Notes · Lecture Notes
Lecture 14: Factor models
Lecture Notes · Lecture Notes
Lecture 15: Factor models part 2
Lecture Notes · Lecture Notes
Lecture 16: Empirical processes
Lecture Notes · Lecture Notes
Lecture 17: Unit roots
Lecture Notes · Lecture Notes
Lecture 18: More non-stationarity
Lecture Notes · Lecture Notes
Lecture 19: Breaks and cointegration
Lecture Notes · Lecture Notes
Lecture 20: Cointegration
Lecture Notes · Lecture Notes
Lecture 21: Filtering, state space models, Kalman filter
Lecture Notes · Lecture Notes
Lecture 22: State-space models, ML estimation, DSGE models
Lecture Notes · Lecture Notes
Lecture 25: MCMC: Metropolis Hastings Algorithm
Lecture Notes · Lecture Notes
Lecture 26: MCMC: Gibbs sampling
MIT OpenCourseWare, Prof. Anna Mikusheva, 14.384 Time Series Analysis, Fall 2013.
License: CC BY-NC-SA 4.0 · Edition: Fall 2013 · Open the original course on OCW ↗
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