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.

We analyze how skill transferability and the local industry mix affect the adjustment costs of workers hit by a trade shock. Using German administrative data and novel measures of economic distance we construct an index of labor market absorptiveness that captures the degree to which workers from a particular industry are able to reallocate into other jobs. Among manufacturing workers, we find that the earnings loss associated with increased import exposure is much higher for those who live in the least absorptive regions. We conclude that the local industry composition plays an important role in the adjustment processes of workers.

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.

Promoting green product consumption is one important element in building a sustainable society. Yet green products are usually more costly. In times of high inflation, not only budget constraints but also the fear that prices will continue to rise might dampen green product consumption and, hence, limit the effectiveness of exerted efforts to promote sustainable behaviors. To test this suggestion, we conducted a Germany-wide survey with almost 1,200 respondents, followed by a framed field experiment (N=500) to confirm causality. In the survey, respondents’ stated “green” purchasing behavior is, as to be expected, positively correlated with concerns about climate change. It is also negatively correlated with concerns about future inflation and energy costs, but after controlling for observable characteristics such as income and educational level only the correlation with concerns about future prices remains significant. This result is driven by individuals with below-median environmental attitude. In the framed field experiment, we use the priming method to manipulate the saliency of inflation concerns. Whereas sizably relaxing the budget constraint (i.e., by 50 percent) has no impact on the share of organic products in participants’ baskets, the priming significantly decreases the share of organic products for individuals with below-median environmental attitude, similar to the survey data.

We provide first evidence for the long-run causal impact that Chinese imports to European regions had on voting outcomes and revisit earlier estimates of the short-run impact for a methodological reason. The fringes of the political spectrum gained ground many years after the China shock plateaued and, unlike an earlier study by Colantone and Stanig (2018b), we do not find any robust evidence for a short-run effect on far-right votes. Instead, far-left and populist parties gained in the short run. We identify persistent long-run effects of import shocks on voting. These effects are biased towards populism and, to a lesser extent, to the far-right.