基礎科目（b）：エビデンス構築手法論 選択必修科目
| 科目名 | 15. | Data Science for Practical Economic Research 開設研究科：公共政策学教育部 科目番号：5123038 |
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| 国際金融・開発研究：経済学系（Data Science for Practical Economic Research） 開設研究科：公共政策学 博士課程 科目番号：5171023 |
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| 科学技術政策研究：経済学系（Data Science for Practical Economic Research） 開設研究科：公共政策学 博士課程 科目番号：5173105 |
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| <応用計量経済>Data Science for Practical Economic Research 開設研究科：経済学研究科 科目番号：291324-02 |
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| 担当教員 | KUCHERYAVYY Konstantin | |
| 配当学期 ／時間 |
S2 ／月4限[14:55-16:40]、水4限[14:55-16:40] |
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| 使用言語 | 英語 | |
| 単位数 | 2単位 | |
| 教室 | 授業の実施形態（教室の場合は教室名、オンラインの場合はURL）については、UTASまたはITC-LMSから科目ごとに確認してください。 | |
| 解説 | 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. |