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Deep Learning for Economists

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  • Melissa Dell

Abstract

Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative AI, and embedding models. Applications include classification, document digitization, record linkage, and methods for data exploration in massive scale text and image corpora. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points.. The review is accompanied by a companion website, EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications.

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  • Melissa Dell, 2024. "Deep Learning for Economists," Papers 2407.15339, arXiv.org, revised Nov 2024.
    • Handle: RePEc:arx:papers:2407.15339
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    3. Beach, Brian & Hanlon, W. Walker, 2022. "Historical Newspaper Data: A Researcher's Guide and Toolkit," CEPR Discussion Papers 17366, C.E.P.R. Discussion Papers.
    4. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    5. Ran Abramitzky & Leah Boustan & Katherine Eriksson & James Feigenbaum & Santiago Pérez, 2021. "Automated Linking of Historical Data," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 865-918, September.
    6. Abhishek Arora & Xinmei Yang & Shao-Yu Jheng & Melissa Dell, 2023. "Linking Representations with Multimodal Contrastive Learning," Papers 2304.03464, arXiv.org, revised Jun 2024.
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    Cited by:

    1. Samuel Chang & Andrew Kennedy & Aaron Leonard & John List, 2024. "12 Best Practices for Leveraging Generative AI in Experimental Research," Artefactual Field Experiments 00796, The Field Experiments Website.
    2. Jésus Fernández-Villaverde & Galo Nuño & Jesse Perla & Jesús Fernández-Villaverde, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," CESifo Working Paper Series 11448, CESifo.
    3. Pablo Ottonello & Wenting Song & Sebastian Sotelo, 2024. "An Anatomy of Firms’ Political Speech," Staff Working Papers 24-37, Bank of Canada.

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