Deep Learning for Economists
It provides a practical guide for economists to apply deep learning to data exploration and analysis tasks, though it is incremental as a review paper.
This review introduces deep learning methods for economists to extract structured information from large-scale unstructured text and image datasets, enabling applications like detecting economic activity in satellite images and analyzing social media or firm filings.
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.