Basic courses B: Evidence Development Methodologies Required Electives
| Title | 15. | Data Science for Practical Economic Research Held by the Graduate School of: Public Policy Number: 5123038 |
|---|---|---|
| Held by the Graduate School of: Public Policy / Doctoral Number: 5171023 |
||
| Held by the Graduate School of: Public Policy / Doctoral Number: 5173105 |
||
| Held by the Graduate School of: Economics Number: 291324-02 |
||
| Instructor | KUCHERYAVYY Konstantin | |
| Schedule | S1S2 / Mon. 4th[14:55-16:40], Wed. 4th[14:55-16:40] |
|
| Language | English | |
| Credit | 2 | |
| Room | Please check the venue (online / classroom location) by each course on UTAS or ITC-LMS. | |
| Abstract | Despite its name, this class is on forecasting methods in economics and applications of machine learning methods to forecasting. A typical class on machine learning focuses on cross-sectional data, leaving almost no space for a discussion of how to work with time series data and how to make forecasts with such data. The purpose of this class is to cover this gap. This class might be useful for students who plan to work at financial companies and government entities tasked with making forecasts. We will closely follow the textbook by G. Elliott and A. Timmermann "Economic Forecasting". The book is quite advanced and requires good understanding of probability and statistics. During the lectures, we will cover chapters from this textbook and perform hands-on sessions. The required programming language is Python. Students taking this class will be assumed to be familiar with basics of Machine Learning, probability and statistics, as well as programming in Python. |