In many set-identified models, it is difficult to obtain a tractable characterization of the identified set. Therefore, researchers often rely on nonsharp identification conditions, and empirical results are often based on an outer set of the identified set. This practice is often viewed as...

Evaluating policy in imperfectly competitive markets requires understanding firm behavior. While researchers test conduct via model selection and assessment, we present the advantages of Rivers and Vuong (2002) (RV) model selection under misspecification. However, degeneracy of RV invalidates...

Modeling policy effects in the context of high-dimensional data requires a balanced consideration of omitted interaction bias and overfitting problems. This paper investigates the role of machine learning algorithms in stabilizing estimates and demonstrates the possible regularization bias...

This paper develops the nonparametric identification of models with production complementarities, worker-firm specific disutility of labor and search frictions. Mobility in the model is subject to preference shocks, and we assume that firms can write wage contracts. We develop a constructive...

Modeling of non-carbon dioxide (non-CO2) greenhouse gas (GHG) emissions in integrated assessment models (IAMs) is critical to achieving long-term climate targets. This study selects nine IAMs to reveal the modeling mechanisms and mitigation of non-CO2 GHGs. It further explores the impact of...

The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim...