ECON 5440

Time Series in Economics

Lec 01 selected

This is a course in econometrics that focuses on time series models. The topics cover estimation and inference procedures for univariate and multivariate time series models with stationary and nonstationary data, including stationary univariate time series models (ARMA), unit-root testing, vector autoregressive and vector error correction models, autoregressive heteroscedasticity (ARCH/GARCH), and Markov switching models.

Sections

Lecture

From the syllabus

From the Winter 2026 syllabus by Christos Ntantamis. Winter 2027 is taught by Kotlyarova Y., so details may differ. Open the PDF

Grading

20%Data assignments (5)Lowest assignment dropped
15%Simulation studies (2)Choose two topics
10%ReportLiterature review or summary
10%Class presentation
15%Midterm examTentatively February 23
30%Final examTheory and computer application

Textbooks

  • Introduction to Econometrics, 4th Edition, James H. Stock and Mark W. Watson
  • Introductory Econometrics: A Modern Approach, 7th Edition, Jeffrey M. Wooldridge
  • Time Series Analysis, Hamilton (optional)
  • The Econometric Modeling of Financial Time Series, Mills and Markellos (optional)

Course rules

Late work
Assignments lose 20% per day late unless an approved exception applies.
Missed assignment
Contact the instructor before the deadline and submit an SDA; its weight is shared among the other assignments.
Missed midterm
Contact the instructor before the exam and submit an SDA; the midterm weight moves to the final exam.
Missed final
A valid reason requires immediate notification; an opportunity to write a make-up exam is provided.
Exam rules
Exams are closed book, and students must score at least 60% on the final to pass.
Passing grade
Graduate students must earn at least 70% overall to pass.
SDA limit
The Student Declaration of Absence form may be used only twice during the term.
Academic integrity
Assignments and reports are individual work; collaboration and generative AI use are prohibited.

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