1  Introduction

This is a book created using Quarto and includes all examples as executable code.

See Blake and Mumtaz (2017).

1.1 Reading is good for you

For me, the best (although slightly dated) text is Hastie, Tibshirani, and Friedman (2009) The Elements of Statistical Learning and the best source for the mathematics, with an easy-reading version by some of the same authors James et al. (2021) Introduction to Statistical Learning.

I also rather like Boehmke and Greenwell (2019) Hands-On Machine Learning with R which is something of a cookbook rather than a technical manual but with wide scope. Taddy (2019) is more elementary.

On text, just read Silge and Robinson (2017) Text Mining with R: A Tidy Approach and then Hvitfeldt and Silge (2021) Supervised Machine Learning for Text Analysis in R. That’s it.

Two books I would solidly recommend to make us all into better statisticians and not just econometricians are Gelman, Hill, and Vehtari (2019) Regression and Other Stories, and McElreath (2020) Statistical Rethinking.

Blake, Andrew P, and Haroon Mumtaz. 2017. Applied Bayesian Econometrics for Central Bankers. Revised. Technical Books. Centre for Central Banking Studies, Bank of England. https://www.bankofengland.co.uk/-/media/boe/files/ccbs/resources/applied-bayesian-econometrics-for-central-bankers-updated-2017.pdf.

Boehmke, Brad, and Brandon M. Greenwell. 2019. Hands-on Machine Learning with R. The R Series. Boca Raton: Chapman & Hall/CRC. https://bradleyboehmke.github.io/HOML/.

Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2019. Regression and Other Stories. Cambridge: Cambridge University Press. http://www.stat.columbia.edu/~gelman/regression.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York, NY: Springer. https://web.stanford.edu/~hastie/ElemStatLearn/.

Hvitfeldt, Emil, and Julia Silge. 2021. Supervised Machine Learning for Text Analysis in R. Chapman & Hall: CRC Press. https://smltar.com/.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in R. 2nd ed. Springer Texts in Statistics. New York, NY: Springer. https://www.statlearning.com/.

McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. 2nd ed. Abingdon, Oxfordshire: CRC Press. https://github.com/rmcelreath/rethinking.

Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. O’Reilly. https://www.tidytextmining.com/.

Taddy, Matt. 2019. Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions. New York, NY: McGraw-Hill Education. https://github.com/TaddyLab/BDS.