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...

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 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...

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 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...

We construct new technology indicators using textual analysis of patent documents and occupation task descriptions that span almost two centuries (1850-2010). At the industry level, improvements in technology are associated with higher labor productivity but a decline in the labor share....

Text data is ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text is often selected--journalists, speechwriters, and others craft messages to target their audiences' limited attention. We develop an economically motivated high dimensional...

An ever increasing share of human interaction, communication, and culture is recorded as digital text. We provide an introduction to the use of text as an input to economic research. We discuss the features that make text different from other forms of data, offer a practical overview of relevant...