Iron Curtain and Big Data are two words usually used to denote completely two different eras. Yet, the context the former offers and the rich data source the latter provides, enable the causal identification of the effect of networks on migration. Academics in countries behind the Iron Curtain...

We use machine-learning methods to study the features and origins of the Baconian program, a cultural and methodological paradigm viewed as providing the intellectual roots for modern economic growth. After building a machine-readable corpus of Bacon's works, we estimate a structural topic...

Research about narratives’ role in economics is scarce, while real word experience and research in other sciences suggest they matter a lot. This article proposes a view and methodology for quantifying the epidemiology of media narratives relevant to business cycles in the US, Japan, and...

Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading driver of antibiotic resistance. We train a machine learning algorithm on administrative and microbiological laboratory data from Denmark to predict...

The importance of units with pervasive impacts on a large number of other units in a network has become increasingly recognized in the literature. In this paper we propose a new method to detect such influential or dominant units by basing our analysis on unit-specific residual error variances...

Formal conceptions of state capacity have mostly focused on indirect measures of state capacity – by, for instance, using the state's fiscal or extractive capacity as a proxy for its overall capacity. Yet, this input or extractive view of state capacity falls short, especially since...

Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating...

The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as "data" in a downstream econometric model. We establish theoretical arguments for why...

Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised...