Australian ETF assets soar by 50% in a year
Total rose from A$152bn at the end of September 2023 to A$227bn at the same point this year
Using structural models to understand macroeconomic tail risks
Understanding asymmetric risks in macroeconomic variables is challenging. Most structural models used for policy analysis are linearised and therefore cannot generate asymmetries such as those documented in the empirical growth-at-risk (GaR) literature. This report examines how structural models can incorporate non-linearities to generate tail risks. The first part reviews the various extensions to dynamic stochastic general equilibrium (DSGE) models and the computational challenges involved in accounting for risk distributions.
The 2026 Commonwealth Games will create an economic model that allows smaller nations to step up and host
The tension was palpable as we waited to see if Glasgow would rescue the Commonwealth Games for 2026. After the Australian state of Victoria pulled out, the eyes of the Commonwealth turned to Scotland.
Neural Network Effects: Scaling and Market Structure in Artificial Intelligence
As artificial intelligence reshapes our economy, policymakers must act swiftly to prevent a winner-take-all scenario in the rapidly evolving market for AI foundation models.
Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence
The decisions we make now about the governance of AI will have profound implications for the future of our economy and society.
VW fined by UK regulator for treatment of customers in financial trouble
Carmaker’s lending arm hit with £5.4mn penalty and agrees to pay £21.5mn to 110,000 borrowers
Inputs in distress: geoeconomic fragmentation and firms’ sourcing
We study how disruptions to the supply of foreign critical inputs (FCIs) —that is, inputs primarily sourced from extra-EU countries with highly concentrated supply, advanced technology products, or which are key to the green transition —might affect value added at different levels of aggregation. Using firm-level customs and balance sheet data for Belgium, France, Italy, Slovenia and Spain, our framework allows us to assess how much geoeconomic fragmentation might affect European economies differently.
Variational inference for Bayesian panel VAR models
We study the application of approximate mean field variational inference algorithms to Bayesian panel VAR models in which an exchangeable prior is placed on the dynamic parameters and the residuals follow either a Gaussian or a Student-t distribution. This reduces the estimation time of possibly several hours using conventional MCMC methods to less than a minute using variational inference algorithms. Next to considerable speed improvements, our results show that the approximations accurately capture the dynamic effects of macroeconomic shocks as well as overall parameter uncertainty.
Variational inference for Bayesian panel VAR models
We study the application of approximate mean field variational inference algorithms to Bayesian panel VAR models in which an exchangeable prior is placed on the dynamic parameters and the residuals follow either a Gaussian or a Student-t distribution. This reduces the estimation time of possibly several hours using conventional MCMC methods to less than a minute using variational inference algorithms. Next to considerable speed improvements, our results show that the approximations accurately capture the dynamic effects of macroeconomic shocks as well as overall parameter uncertainty.
Inputs in distress: geoeconomic fragmentation and firms’ sourcing
We study how disruptions to the supply of foreign critical inputs (FCIs) —that is, inputs primarily sourced from extra-EU countries with highly concentrated supply, advanced technology products, or which are key to the green transition —might affect value added at different levels of aggregation. Using firm-level customs and balance sheet data for Belgium, France, Italy, Slovenia and Spain, our framework allows us to assess how much geoeconomic fragmentation might affect European economies differently.
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