UGA Bulletin Logo

Time Series Analysis

Analytical Thinking
Critical Thinking

Course Description

An introduction to the econometric analysis of time series data, with a focus on causal inference and forecasting. The course applies fundamental models of stationary and non-stationary stochastic processes to real world problems in economics, finance and other disciplines.

Additional Requirements for Graduate Students:
In addition to submitting all work required of undergraduate students, graduate students in the course must submit a completed research project on a topic related to the course material that demonstrates a broader and deeper understanding of that material than is required of undergraduate students. The research paper will incorporate a thorough review of the relevant literature, will rely on extensive knowledge of the quantitative tools developed in the course, and will be assessed in terms of demonstrated competence in synthesizing, criticizing, and extending knowledge in the field. Overall, graduate students will be held to the high standards of scholarship that guide the Graduate School and will be expected to exhibit a mastery of skills that goes beyond the learning outcomes for undergraduate students.

Athena Title

Time Series Analysis

Undergraduate Prerequisite

ECON(MARK) 4750/6750

Graduate Prerequisite

ECON(MARK) 4750/6750

Semester Course Offered

Offered every year.

Grading System

A - F (Traditional)

Student learning Outcomes

  • Students will recognize the unique challenges of analyzing data with dependent observations.
  • Students will be able to characterize patterns and trends of data exhibited in time series graphs.
  • Students will be able to use fundamental models of stochastic processes to project future realizations of time series data and to make causal inference about dynamic behavior.
  • Students will be able to critically assess decisions and policies that have dynamic effects on actions and outcomes in the future.
  • Students will be introduced to the connections between time series models and artificial intelligence.

Topical Outline

  • The nature of time series
  • Fundamental concepts of time series
  • ARIMA models
  • Dynamic Regression models
  • Vector autoregression
  • Causal inference in VAR models
  • Cointegration
  • Dynamic factor models

Institutional Competencies Learning Outcomes

Analytical Thinking

The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.


Critical Thinking

The ability to pursue and comprehensively evaluate information before accepting or establishing a conclusion, decision, or action.


Syllabus

Jump to Section
publish