Readings and references

Textbooks on machine learning

Hastie, T., Tibshirani, R., & Wainwright, M. J. (2015). Statistical Learning with Sparsity: The Lasso and Generalizations Boca Raton: CRC Press, Taylor & Francis.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). New York: Springer-Verlag.

Bühlmann, P., & Van de Geer, S. (2011). Statistics for High-Dimensional Data. Berlin, Heidelberg: Springer-Verlag.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An Introduction to Statistical Learning with Applications in R. Springer New York.

Survey articles on machine learning and econometrics

Athey, Susan (2018). The Impact of Machine Learning on Economics. NBER Working Paper.

Belloni, A., Chernozhukov, V. and Hansen, C. 2015. High-dimensional methods and inference on structural and treatment effects. Journal of Economic Perspectives 28(2):29-50.

Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106.

Varian, Hal R. 2014. Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28 (2): 3-28.

Video lectures

Summer Institute 2013 Econometric Methods for High-Dimensional Data with Victor Chernozhukov, Matthew Gentzkow, Christian Hansen, Jesse Shapiro, Matthew Taddy.

Machinistas meet randomistas: useful ML tools for empirical researchers by Esther Duflo.

Video lectures on Statistical Learning by Trevor Hastie and Rob Tibshirani

Further references

For further references, please see references in our help files and in our Working Paper.