Time Series Analysis: Advanced Topics

Instructor(s):

  • Mark Pickup, Simon Fraser University and the University of Oxford
  • Patrick Brandt, University of Texas at Dallas

This course covers advanced topics in time series analysis. Topics will include vector autoregression models, vector error correction models, state-space models, dynamic factor models, Bayesian vector autoregression models, count time series, Markov-switching and change-point models, and forecast evaluation.

This course is intended for those who have taken the four-week workshop on Time Series Analysis, the one-week workshop on Time Series Analysis: An Introduction, or the equivalent.

A sound background in time series fundamentals is assumed. The course will make use of basic matrix algebra. The lab component of this course will employ STATA and R. Familiarity with STATA is assumed but a STATA crash course will be provided outside the lecture on day two. If you are unfamiliar with R or STATA, we suggest that you attend one of the many R tutorial sessions or lectures offered as part of the Summer Program.

Fees: Consult the fee structure.

Tags: Time Series, vector autoregression, count time series, VAR, dynamic factor model, forecasting, vector error correction model, state-space model

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): July 21 - August 15

Time: 3:00 PM - 5:00 PM

Instructor(s):

  • Mark Pickup, Simon Fraser University and the University of Oxford
  • Patrick Brandt, University of Texas at Dallas

Syllabus:

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