Syndicated loan data provided by DealScan is an essential input in banking research to answer urging questions on bank lending, e.g., in the presence of financial or geopolitical shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice, and we provide guidelines for applied research.

Using brand-level sales data from Chinese e-commerce platforms, this study examines how geopolitical tensions affect multinational brands operating in China. Merging these sales data with a U.S.–China tension index, we use panel regressions and local projections to show that rising tensions significantly reduce the market share of U.S. brands in China relative to brands from other countries, with the effects persisting for up to 12 months. An event study employing a difference-in-differences framework, centered on brand-specific incidents of political tension with China, reveals similar market share declines among affected brands, highlighting consumer sentiment as a key transmission channel.

This study provides new evidence on the impact of the EU Cohesion Policy on income growth in less developed regions. Our panel includes data from all European regions for the years 1989-2020. Using a fuzzy Regression Discontinuity Design, we model treatment dynamics by applying a random effects estimator. Based on digitized historical data, we precisely replicate the policy rule and correctly classify the regions’ eligibility status. Results show that the policy has a moderate positive effect on GDP per capita growth in the targeted regions.

We exploit an information shock related to the German Supply Chain Due Diligence Act and use detailed customs data to analyze how smaller, non-listed firms respond when expecting accountability for externalities beyond their organizational boundaries. Product-level regressions reveal a substantial reduction in imports from high ESG-risk production sectors. Adjustments occur mainly at the extensive margin, indicating that firms cut ties with high-risk suppliers. The product-level results translate into meaningful changes in overall international procurement for firms with Big Four auditors. Our findings suggest potential limits to mandates requiring firms to integrate broad sustainability considerations into operational decisions.

This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast’s contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.