Deep Learning for Economists

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 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 artificial intelligence (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 regularly updated companion website, EconDL ( https://econdl.github.io/ ), with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications. (JEL C38, C45, C88, D83)

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 72 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.