TILM3589 Nonlinear Time Series Analysis (6 cr)
Cooperation network course
Network: Cross-institutional studies in advanced courses in mathematics and statistics
This course is offered through the Network for Advanced Studies in Mathematics. These studies are available for the following degree students:
- Bachelor's Degree Programme in Mathematics
- Master's Degree Programme in Mathematics
- Bachelor's Degree Programme in Mathematics (Subject Teacher)
- Master's Degree Programme in Mathematics (Subject Teacher)
- Bachelor's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Master's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Doctoral Programme in Mathematics and Statistics
- Doctoral Programme in Mathematics and Science (Specialication in Mathematics)
Grading scale:
0-5
Description
This course introduces modern nonlinear time series models for the conditional mean, conditional variance and limited dependent variables, including model specification, estimation, evaluation and forecasting, as well as connections to statistical learning and machine learning in connection to time series data.
All the considered nonlinear models and methods are general and can be applied in any field but are highly important especially in various macroeconomic and financial applications.
Learning outcomes
After the course, the student
- masters a wide range of advanced nonlinear time series models and techniques, including statistical inference and forecasting therein, and knows linkages to modern statistical learning and machine learning when analyzing time series data.
- has methodological and practical skills for conducting empirical nonlinear time series analysis independently.
Description of prerequisites
In addition to the basic course on time series analysis (such as TILM3541) or equivalent, students are expected to have the basic knowledge of linear regression analysis (including the basics of statistical inference), or introductory econometrics in economics and finance, and the prerequisites for those courses.
In addition, it is strongly recommended to complete Multiple Time Series Analysis (TILM3586, and/or Macroeconometrics TILM3609) before or at the same time as this course due to the datasets and applications discussed in the course. Similarly, it is recommended to complete courses Advanced Regression Analysis and Statistical Learning (TILM3587 or TILM3622) and Advanced Statistical Learning (TILM3620), or similar courses, on statistical (machine) learning regarding the essential points of these methods.