大学院
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最終更新日：2024年10月18日
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最終更新日：2024年10月18日
授業計画や教室は変更となる可能性があるため、必ずUTASで最新の情報を確認して下さい。
UTASにアクセスできない方は、担当教員または部局教務へお問い合わせ下さい。
Data Science for Practical Economic Research
Data Science for Practical Economic Research
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.
Students taking this class will be assumed to be familiar with basics of Machine Learning, probability and statistics, as well as programming in Python.
時間割／共通科目コード
コース名
教員
学期
時限
291324-02
GEC-EC6322L3
Data Science for Practical Economic Research
Kucheryavyy Konstantin
月曜4限、水曜4限
マイリストに追加
マイリストから削除
講義使用言語
英語
単位
2
実務経験のある教員による授業科目
NO
他学部履修
可
開講所属
経済学研究科
授業計画
Preliminary plan of lectures (subject to change):
Lecture 1: Loss Functions.
Lecture 2: The Parametric Forecasting Problem.
Lecture 3: Classical Estimation of Forecasting Models; Bayesian Forecasting Methods.
Lecture 4: Model Selection.
Lecture 5: Univariate Linear Prediction Model.
Lecture 6: Vector Autoregressions.
Lecture 7: Forecasting in a Data-Rich Environment.
Lecture 8: Nonparameteric Forecasting Methods.
Lecture 9: Binary Forecasts.
Lecture 10: Forecast Combinations.
Lecture 11: Desirable Properties of Forecasts.
授業の方法
Lectures. Demo programs.
成績評価方法
Grade will be based on homeworks.
教科書
– G. Elliott and A. Timmermann "Economic Forecasting" (Princeton University Press, 2006)
- F.X. Diebold "Forecasting in Economics, Business, Finance and Beyond" (University of Pennsylvania, 2017)
- Peter Fuleky (editor) "Macroeconomic Forecasting in the Era of Big Data: Theory and Practice" (Springer, 2020)
参考書
NA
履修上の注意
- Good command of Python is assumed.
- Students are assumed to be familiar with basics of Machine Learning.
- Students are assumed to have taken basic probability and statistics classes.
