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.

I estimate a dynamic stochastic general equilibrium (DSGE) model for the United States that incorporates oil market shocks and risk shocks working through credit market frictions. The findings of this analysis indicate that risk shocks play a crucial role during the Great Recession and the Dot-Com bubble but not during other economic downturns. Credit market frictions do not amplify persistent oil market shocks. This result holds as long as entry and exit rates of entrepreneurs are independent of the business cycle.

New Zealand has experienced poor productivity performance over the last two decades. Factors often cited as reasons behind this are the small size of the domestic market and distance to international partners and markets. While the distance reason is one that is fairly insurmountable, there are a number of other small advanced economies that also face similar domestic market constraints. This study compares the relative performance of New Zealand’s firms to those economies using novel cross-country microdata from CompNet. We present stylised facts for New Zealand relative to the economies of Belgium, Denmark, Finland, Netherlands and Sweden based on average productivity levels, as well as benchmarking laggard, median and frontier firms. This research also employs an analytical framework of technology diffusion to evaluate the extent of productivity convergence, and the impact of the productivity frontier on non-frontier firm performance. Additionally, both labour and capital resource allocation are compared between New Zealand and the other small advanced economies. Results show that New Zealand’s firms have comparatively low productivity levels and that its frontier firms are not benefiting from the diffusion of best technologies outside the nation. Furthermore, there is evidence of labour misallocation in New Zealand based on less labour-productive firms having disproportionally larger employment shares than their more productive counterparts. Counter-factual analysis illustrates that improving both technology diffusion from abroad toward New Zealand’s frontier firms, and labour allocation across firms within New Zealand will see sizable productivity gains in New Zealand.