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Time Series Analysis has wide applicability in economic and financial fields but also to geophysics, oceanography, atmospheric science, astronomy, engineering, among many other fields of practice. This course will illustrate time series analysis using many applications from these fields.
In this course, students will learn standard time series analysis topics such as modeling time series using regression analysis, univariate ARMA/ARIMA modelling, (G)ARCH modeling, Vector Autoregressive (VAR) model along with forecasting, model identification and diagnostics. Students will be given fundamental grounding in the use of such widely used tools in modeling time series.
Throughout this course, students will be exposed to not only fundamental concepts of time series analysis but also many data examples using the R statistical software. Thus by the end of this course, students will also be familiar with the implementation of time series models using the R statistical software along with interpretation for the results derived from such implementations.
This class is more about the opportunity for individual discovery than it is about mastering a fixed set of techniques.
Overview
Syllabus
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Weeks 1-3: Introduction to basic concepts of time series analysis
Weeks 4-6: Introduction to the ARMA Modeling and its extension, including illustration with data examples
Week 7: Midterm 1 Examination
Weeks 8-10: Introduction to most popular multivariate time series model, the VAR model, with data examples
Weeks 11-13: Introduction to GARCH modeling for heteroskedasticity, with data examples
Week 14: Midterm 2 Examination
Weeks 15: Overview of the time series models introduced in this course along with brief description of other time series methods
Week 16: Final Examination
