References

Alonso Robisco, Andrés, and José Manuel Carbó Martínez. 2022. “Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction.” Financial Innovation 8 (1). https://doi.org/10.1186/s40854-022-00366-1.

Aruoba, S. Boragan, and Thomas Drechsel. 2022. “Identifying Monetary Policy Shocks: A Natural Language Approach.” CEPR Discussion Paper DP17133. CEPR.

Bank for International Settlements. 2021. “Machine learning applications in central banking.” IFC Bulletin 57. https://www.bis.org/ifc/publ/ifcb57.pdf.

Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. Edited by Michael Jordan, Jon Kleinberg, and Bernhard Schölkopf. Information Science and Statistics. Springer Science+Business Media, LLC. https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf.

Fernández-Villaverde, Jesús, Samuel Hurtado, and Galo Nuño. 2023. “Financial Frictions and the Wealth Distribution.” Econometrica 91 (3): 869–901. https://doi.org/10.3982/ecta18180.

Fernández-Villaverde, Jesús, Joël Marbet, Galo Nuño, and Omar Rachedi. 2024. “Inequality and the Zero Lower Bound.” Working Paper 2407. Banco de España.

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http://www.deeplearningbook.org.

Gorodnichenko, Yuriy, Tho Pham, and Oleksandr Talavera. 2023. “The Voice of Monetary Policy.” American Economic Review 113 (2): 548–84. https://doi.org/10.1257/aer.20220129.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning - Data Mining, Inference, and Prediction. Second Edition. Springer.

Kaji, Tetsuya, Elena Manresa, and Guillaume Pouliot. 2023. “An Adversarial Approach to Structural Estimation.” Econometrica 91 (6): 2041–63. https://doi.org/10.3982/ecta18707.

Kase, Hanno, Leonardo Melosi, and Matthias Rottner. 2022. “Estimating Nonlinear Heterogeneous Agents Models with Neural Networks.” Federal Reserve Bank of Chicago. https://doi.org/10.21033/wp-2022-26.

Maliar, Lilia, Serguei Maliar, and Pablo Winant. 2021. “Deep learning for solving dynamic economic models.” Journal of Monetary Economics 122 (September): 76–101. https://doi.org/10.1016/j.jmoneco.2021.07.004.

McCulloch, Warren S., and Walter Pitts. 1943. “A logical calculus of the ideas immanent in nervous activity.” The Bulletin of Mathematical Biophysics 5 (4): 115–33. https://doi.org/10.1007/bf02478259.

McKinney, Wes. 2022. Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter. Third Edition. O’Reilly Media. https://wesmckinney.com/book/.

Microsoft. 2024. “Deep learning vs. machine learning in Azure Machine Learning.” Website. https://learn.microsoft.com/en-us/azure/machine-learning/concept-deep-learning-vs-machine-learning?view=azureml-api-2.

Mitchell, Tom. 1997. Machine Learning. McGraw Hill. https://www.cs.cmu.edu/~tom/mlbook.html.

Murphy, Kevin P. 2012. Machine Learning: A Probabilistic Perspective. Cambridge: MIT Press. https://probml.github.io/pml-book/book0.html.

———. 2022. Probabilistic Machine Learning: An Introduction. MIT Press. https://probml.github.io/pml-book/book1.html.

———. 2023. Probabilistic Machine Learning: Advanced Topics. MIT Press. https://probml.github.io/pml-book/book2.html.

Nielsen, Michael. 2019. Neural Networks and Deep Learning. http://neuralnetworksanddeeplearning.com.

Rosenblatt, F. 1958. “The perceptron: A probabilistic model for information storage and organization in the brain.” Psychological Review 65 (6): 386–408. https://doi.org/10.1037/h0042519.

Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. Second Edition. MIT Press. http://incompleteideas.net/book/the-book-2nd.html.

Varian, Hal R. 2014. “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives 28 (2): 3–28. https://doi.org/10.1257/jep.28.2.3.