“The Cost of Closing the Strait of Hormuz: Energy Bottlenecks and Global Food Security” by Julian Hinz, Hendrik Mahlkow, Robin Sogalla, and Gerald Willmann.
“In March 2026, the Strait of Hormuz is closed. The shutdown blocks roughly one-fifth of the world’s oil and one-quarter of its liquefied natural gas, triggering severe welfare losses in energy-dependent developing countries worldwide. Standard trade models underestimate the impact because they miss the bottleneck mechanism: energy disruptions cascade through chemicals and fertilizer production into food prices, amplifying losses for the world’s poorest countries. Developing countries that depend on imported energy and fertilizers—particularly in South Asia, sub-Saharan Africa, and the Middle East—face the steepest food price increases and welfare losses. The aggregate global costs are moderate, but the burden falls disproportionately on the world’s poorest: the USA loses just −0.07%, while countries in South Asia and Africa face losses 10–20 times larger. A prolonged closure allows some market adjustment, but structural damage persists—and the timing during peak Northern hemisphere planting season compounds the food security risk.”
“BISTRO: a general purpose oracle for macroeconomic time series” by Batuhan Koyuncu, Byeungchun Kwon, Marco Jacopo Lombardi, Fernando Perez-Cruz, and Hyun Song Shin.
“This article introduces the BIS Time-series Regression Oracle (BISTRO), a general purpose time series model for macroeconomic forecasting. Building on the transformer architecture underlying LLMs, BISTRO is fine-tuned on the large repository of macroeconomic data maintained at the BIS. We put the model through its paces by assessing how well it forecasts the 2021 inflation surge. In contrast to standard benchmarks, which mechanically project a reversion to the mean, BISTRO correctly anticipates the persistence of the inflation wave. This highlights its ability to adapt to unfamiliar patterns in the data. Thus, BISTRO holds promise for producing reliable baseline forecasts and for scenario analysis.”
“Forecasting the Economic Effects of AI” by Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin Bryan, Basil Halperin, Todd Jones, Connacher Murphy, Phil Trammell, Matt Reynolds, Dan Mayland, Ria Viswanathan, Ananaya Mittal, Rebecca Ceppas de Castro, Josh Rosenberg, and Philip E. Tetlock.
“We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters.”
Global Economy: The global economy has shown resilience amid headwinds, but the dampening effects of high oil prices, supply chain stress, geopolitical uncertainty, and potential food shortages are considerable.
We expect global economic output to increase by 2.9 percent in 2026 (-0.2 percent compared to our previous forecast). In a more severe scenario with oil prices persistently above $100 per barrel, stagflationary scenarios would become relevant for several countries. Global GDP growth would then drop to 2.6 percent in 2026.
