Machine Learning in Economics
Кучерявый Константин Сергеевич
- 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.
- The assignmentthe assignment after 4 weeks of classes
- Final projectThe 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.
- 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
- Bruce E. Hansen. (2013). Econometrics. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C0DB9E1E