Kristalina Georgieva, before the start of the IMF-World Bank annual meeting in Singapore, said economic policy chiefs could not keep delaying action. Kristalina Georgieva, before the start of the IMF-World Bank annual meeting in Singapore, said economic policy chiefs could not keep delaying action. Kristalina Georgieva says big economies will have to make'very touch choices' as soaring bond yields hit budgets The head of the International Monetary Fund has called on governments across big economies to tighten their belts as soaring bond yields hit budgets. Speaking in Singapore, the IMF's managing director, Kristalina Georgieva, said global debt-to-GDP ratios were at their highest level since the second world war and on course to hit 100% in the coming years. She said governments could not rely on rapid economic growth to lift the burden of debt - and instead would have to make "very tough political choices".

Why did Trump fire Pam Bondi? Trump's US Fed nominee Warsh vows independence, says he's no'sock puppet' Kevin Warsh, United States President Donald Trump's pick to lead the Federal Reserve, has addressed concerns about his independence pending his appointment to the bank amid fears that Trump could sway his decisions on monetary policy. On Tuesday, Warsh -- who served on the central bank's Board of Governors from 2006 to 2011 -- faced waves of criticism during a confirmation hearing of the Senate Banking Committee where Democrats voiced concerns about the Fed's independence should he be appointed to lead the organisation. "I do not believe the operational independence of monetary policy is particularly threatened when elected officials -- presidents, senators, or members of the House -- state their views on interest rates," Warsh said. "Monetary policy independence is essential. Monetary policymakers must act in the nation's interest . . . Warsh, 56, also called for "regime change" at the US central bank, including a new approach for controlling inflation and a communications overhaul that may discourage his colleagues from saying too much about the direction of monetary policy. Warsh blamed the central bank for an inflation surge after it slashed interest rates to nearly zero in the wake of the COVID-19 pandemic, a move that continues to hurt US households. Concerned by the implications of artificial intelligence for jobs - expected to increase productivity - and prices, he said he would move quickly to see if new data tools could provide better insight on inflation, and would also discourage policymakers from saying too much about where interest rates might be heading. "What the Fed needs are reforms to its frameworks and reforms to its communications," the former Fed governor said. "Too many Fed officials opine about where interest rates should be That is quite unhelpful." Warsh has also long been an advocate for shrinking the Fed's $6.7 trillion balance sheet. In the Tuesday hearing, he said any such plans would take time and must be publicly discussed well in advance. Jai Kedia, a research fellow at the Center for Monetary and Financial Alternatives at the libertarian Cato Institute, told Al Jazeera that there were many "encouraging" signs in Warsh's candidacy. "Warsh is presenting himself as a regime change candidate at a time when the Fed needs serious reform," Kedia noted. "Particularly encouraging was his understanding of the negative effects of QE and his focus on reducing the balance sheet.

Wild fluctuations in the price of gold continued on Wednesday as geopolitical tensions reignited after the US downed an Iranian drone . The precious metal, which is seen as a so-called safe haven for investors in times of uncertainty, shot back above $5,000 (£3,650) an ounce following days of sharp falls. Gold prices had been propelled to record highs by rapid changes in US trade policy, ongoing geopolitical uncertainty and conflict and central banks increasing their purchases of bullion. Wednesday's jump, to $5,061 per ounce, left the price of gold around 80% higher than the same time a year ago. A US military spokesman confirmed the Iranian drone had been shot down after it aggressively approached an American aircraft carrier in the Arabian Sea. Tehran has not commented on Tuesday's incident.

It is extraordinary enough to see the world's top central banker make an unscheduled video statement on social media. My first thought upon seeing the post from the Federal Reserve chair Jerome Powell was: Is this an AI deepfake? That sense did not go away as I listened to what were indeed the real words of the world's most important financial official. The background here is a long-running spat between President Trump and the man responsible for setting interest rates in the US and indirectly much of the rest of the world. In theory, this has officially been about the cost of a renovation project at the Federal Reserve, the US equivalent of the Bank of England.

UK share values'most stretched' since 2008, Bank warns The Bank of England has warned of a sharp correction in the value of major tech companies with growing fears of an artificial intelligence (AI) bubble. It said share prices in the UK are close to the most stretched they have been since the 2008 global financial crisis, while equity valuations in the US are reminiscent of those before the dotcom bubble burst. The central bank's financial stability report warned valuations are particularly stretched for companies focused on AI. It said the growth of the sector in the next five years would be fuelled by trillions of dollars of debt, raising financial stability risks if the value of the companies falls. The Bank of England cited industry figures forecasting spending on AI infrastructure could top $5tn (£3.8tn).

The emergence of generative Artificial Intelligence (AI) has created new opportunities for economic text analysis. This study proposes a Wage Sentiment Index (WSI) constructed with Large Language Models (LLMs) to forecast wage dynamics in Japan. The analysis is based on the Economy Watchers Survey (EWS), a monthly survey conducted by the Cabinet Office of Japan that captures real-time economic assessments from workers in industries highly sensitive to business conditions. The WSI extends the framework of the Price Sentiment Index (PSI) used in prior studies, adapting it specifically to wage related sentiment. To ensure scalability and adaptability, a data architecture is also developed that enables integration of additional sources such as newspapers and social media. Experimental results demonstrate that WSI models based on LLMs significantly outperform both baseline approaches and pretrained models. These findings highlight the potential of LLM-driven sentiment indices to enhance the timeliness and effectiveness of economic policy design by governments and central banks.

Accurately forecasting central bank policy decisions, particularly those of the Federal Open Market Committee (FOMC) has become increasingly important amid heightened economic uncertainty. While prior studies have used monetary policy texts to predict rate changes, most rely on static classification models that overlook the deliberative nature of policymaking. This study proposes a novel framework that structurally imitates the FOMC's collective decision-making process by modeling multiple large language models (LLMs) as interacting agents. Each agent begins with a distinct initial belief and produces a prediction based on both qualitative policy texts and quantitative macroeconomic indicators. Through iterative rounds, agents revise their predictions by observing the outputs of others, simulating deliberation and consensus formation. To enhance interpretability, we introduce a latent variable representing each agent's underlying belief (e.g., hawkish or dovish), and we theoretically demonstrate how this belief mediates the perception of input information and interaction dynamics. Empirical results show that this debate-based approach significantly outperforms standard LLMs-based baselines in prediction accuracy. Furthermore, the explicit modeling of beliefs provides insights into how individual perspectives and social influence shape collective policy forecasts.

We present SimCity, a multi-agent framework that leverages LLMs to model an interpretable macroeconomic system with heterogeneous agents and rich interactions. Unlike classical equilibrium models that limit heterogeneity for tractability, or traditional agent-based models (ABMs) that rely on hand-crafted decision rules, SimCity enables flexible, adaptive behavior with transparent natural-language reasoning. Within SimCity, four core agent types (households, firms, a central bank, and a government) deliberate and participate in a frictional labor market, a heterogeneous goods market, and a financial market. Furthermore, a Vision-Language Model (VLM) determines the geographic placement of new firms and renders a mapped virtual city, allowing us to study both macroeconomic regularities and urban expansion dynamics within a unified environment. To evaluate the framework, we compile a checklist of canonical macroeconomic phenomena, including price elasticity of demand, Engel's Law, Okun's Law, the Phillips Curve, and the Beveridge Curve, and show that SimCity naturally reproduces these empirical patterns while remaining robust across simulation runs.

We present a category theoretical formulation of the Monetary Macroeconomic Accounting Theory (MoMaT) of Menéndez and Winschel [2025]. We take macroeconomic (national) accounting systems to be composed from microeconomic double-entry systems with real and monetary units of accounts. Category theory is the compositional grammar and module system of mathematics which we use to lift micro accounting consistency to the macro level. The main function of money in MoMaT is for the repayment of loans and not for the exchange of goods, bridging the desynchronisation of input and output payments of producers. Accordingly, temporal accounting consistency is at the macroeconomic level. We show that the accounting for macroeconomies organised by a division of labor can be consistent and stable as a prerequisite for risk and GDP sharing of societies. We exemplify the theory by five sectoral agents of Labor and Resource owners, a Company as the productive sector, a Capitalist for profits, and a Bank as the financial sector providing loans to synchronise the micro and the macro levels of an economy. The dynamics is described by eight sectoral macroeconomic bookings in each period demonstrating stable convergence of the MoMaT in numerical simulations. The categorical program implements a consistent evolution of hierarchical loan repayment contracts by an endofunctor. The universal constructions of a limit verify all constraints as the sectoral investment and learning function at the macroeconomic level. The dual colimit computes the aggregated informations at the macro level as usual in the mathematics of transitions from local to global structures. We use visual diagrams to make complex economic relationships intuitive. This paper is meant to map economic to categorical concepts to enable interdisciplinary collaboration for digital twins of monetary accounting systems.

BeforeIT is an open-source software for building and simulating state-of-the-art macroeconomic agent-based models (macro ABMs) based on the recently introduced macro ABM developed in [1] and here referred to as the base model. Written in Julia, it combines extraordinary computational efficiency with user-friendliness and extensibility. We present the main structure of the software, demonstrate its ease of use with illustrative examples, and benchmark its performance. Our benchmarks show that the base model built with BeforeIT is orders of magnitude faster than a Matlab version, and significantly faster than Matlab-generated C code. BeforeIT is designed to facilitate reproducibility, extensibility, and experimentation. As the first open-source, industry-grade software to build macro ABMs of the type of the base model, BeforeIT can significantly foster collaboration and innovation in the field of agent-based macroeconomic modelling. The package, along with its documentation, is freely available at https://github.com/bancaditalia/BeforeIT.jl under the AGPL-3.0.