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Machine Learning in Economics

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
5
Кредиты
Статус:
Курс по выбору
Когда читается:
4-й курс, 2 модуль

Преподаватель


Кучерявый Константин Сергеевич

Course Syllabus

Abstract

One of the goals of machine learning methods is prediction or forecasting. Use of machine learning methods for forecasting in economics has been gaining popularity in recent years. Economists use machine learning methods for forecasting of gross domestic product, inflation, trade flows, and other economic indicators. To forecast such economic indicators, one must properly deal with the time series aspects of these indicators. In this class, we will study the basics of economic forecasting and key machine learning methods used for this.
Learning Objectives

Learning Objectives

  • One of the goals of machine learning methods is prediction or forecasting.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students will learn theoretical and practical aspects of economic forecasting (loss functions, maximum likelihood, frequentist versus Bayesian approach to forecasting, forecast combinations, etc.)
  • Students will learn basic methods used for forecasting of time series data (AR, MA, VAR, etc.)
  • Students will learn basic machine learning methods used for forecasting (LASSO, Ridge, boosting trees, cross-validation, etc.).
  • Students will gain experience with applying the methods to real data.
Course Contents

Course Contents

  • Week 1
  • Week 2
  • Week 3
  • Week 4
  • Week 5
  • Week 6
  • Week 7
Assessment Elements

Assessment Elements

  • non-blocking The assignment
    the assignment after 4 weeks of classes
  • non-blocking Final project
    The course will be finished with a practical course assignment for which you will have two weeks to complete (can be done in groups of 2-3 people max). The assignment will be in jupyter notebooks and will consists of applying the methods learned in class for a practical forecasting problem. Each group will get a different dataset to work on.
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.7 * Final project + 0.3 * The assignment
Bibliography

Bibliography

Recommended Core Bibliography

  • Hamilton, J. D. . (DE-588)122825950, (DE-576)271889950. (1994). Time series analysis / James D. Hamilton. Princeton, NJ: Princeton Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.038453134
  • Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning: Data Mining, Inference, and Prediction. – Springer, 2009. – 745 pp.
  • Rogers, S., & Girolami, M. (2016). A First Course in Machine Learning (Vol. 2nd ed). Milton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1399490
  • Yudi Pawitan, In All Likelihood: Statistical Modelling and Inference Using Likelihood, Oxford University Press (2019) Persistent link to this record (Permalink): http://search.ebscohost.com/login.aspx?direct=true&db=edspub&AN=edp2267145&site=pfi-live

Recommended Additional Bibliography

  • Bruce E. Hansen. (2013). Econometrics. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C0DB9E1E