This paper proposes a new approach to the "factor zoo" conundrum. Instead of applying dimension-reduction methods to a large set of portfolios obtained from sorts on characteristics, I construct factors that summarize the information in characteristics across assets and then sort assets into...

We examine the rise of dark shipping - oil tankers disabling AIS transceivers to evade detection - amid Western sanctions on Iran, Syria, North Korea, Venezuela, and Russia. Using a machine learning-based ship clustering model, we track dark-shipped crude oil trade flows worldwide and detect...

This paper proposes latent factor models for multidimensional panels called 3D-PCA. Factor weights are constructed from a small set of dimension-specific building blocks, which give rise to proportionality restrictions of factor weights. While the set of feasible factors is restricted, factors...

Sparse models, though long preferred and pursued by social scientists, can be ineffective or unstable relative to large models, for example, in economic predictions (Giannone et al., 2021). To achieve sparsity for economic interpretation while exploiting big data for superior empirical...

Beginning in 1997, the Food and Drug Administration (FDA) allowed television advertisements to make major statements about a prescription drug, while referring to detailed drug information on the internet (FDA 1997; 2015). The hope was that consumers would seek additional information online to fully...

This paper provides a data-driven overview of the different online platforms that consumers use to search for books and booksellers, and documents how the use of these platforms is shifting over time. Our data suggest that, as a result of digitization, consumers are increasingly conducting...

We examine whether changes in the local school choice environment affect the amount of information parents collect about local school quality, using data on over 100 million searches from greatschools.org. We link monthly data on search frequency in local "Search Units" to information on changes...

We study whether Amazon engages in self-preferencing on its marketplace by favoring its own brands (e.g., Amazon Basics) in search. To address this question, we collect new micro-level consumer search data using a custom browser extension installed by a panel of study participants. Using this...

This article applies simple methods from computational linguistics to analyze unstructured corporate texts for economic surveillance. We apply text-as-data approaches to earnings conference call transcripts, patent texts, and job postings to uncover unique insights into how markets and firms...

We introduce a general approach for analyzing large-scale text-based data, combining the strengths of neural network language processing and generative statistical modeling to create a factor structure of unstructured data for downstream regressions typically used in social sciences. We generate...