Machine Learning at Central Banks

These are materials for an introduction to machine learning course Belize Central Bank, August 6, 2020. If you have any questions or comments please email me at hersh [at] chapman [dot] edu

Schedule

• 9am – 10am
  • Introduction to Machine Learning
    • Slides
    • Video
• 10:00am – 11am
  • Basic Machine Learning Models
    • Slides
    • Video
• 11:00am – 11:15am
  • Break!
  • Q & A
• 11:15am – 12pm
  • Brief Introduction to R
    • Slides
    • Code
      • Belize_Intro_CV.R
        
      • Belize_Lasso_Ridge.R
      • Intro_to_R.R
    • Video

Text References

• Classic statistical text on machine learning algorithms
  
  • An Introduction to Statistical Learning
• Excellent (free!) book on programming in R
  • Garret Grolemund and Hadley Wickham, R for Data Science

Papers

• Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.
• Chakraborty, C., & Joseph, A. (2017). Machine learning at central banks.
• Hersh, J., & Harding, M. (2018). Big Data in economics. IZA World of Labor, 451-451.
• Hersh, Jonathan; Engstrom, Ryan; Mann, Michael; Martin, Lucia;Mejía, Alejandra, (May, 2020). Mapping Income Poverty in Belize Using Satellite Features and Machine Learning
• Hellwig, Klaus-Peter. (2020). Predicting Fiscal Crises: A Machine Learning Approach. IMF Mimeo
• Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.

Computational Resources

• To use R locally on your machine you must down load both of these
  • R Studio
  • R programming environment
• To run R in the cloud create an account here
  • rstudio.cloud