Month: April 2026

What machines taking over pricing means for central banks

Anthony Savagar, Misa Tanaka and Jagdish Tripathy

With increased availability of big data and computing power, more firms are adopting algorithmic and AI-powered pricing to adjust prices rapidly in response to changing economic conditions over time and across consumers. This post reviews the existing research, draws implications for central banks, and identifies areas for further research on this topic. The research reviewed here was also used to inform Lombardelli and Patel (2026). The existing research suggests that new pricing technologies will lead to faster pass-through of shocks to prices, greater market segmentation, and may improve the inflation-output trade-off for monetary policy makers. To ensure price stability, central banks will need to monitor granular, high-frequency price data to gauge the impact of shocks on prices and inflation expectations.

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Tomorrow’s costs, today’s prices: why expectations matter for inflation

Boromeus Wanengkirtyo, Ivan Yotzov and Mishel Ghassibe

Can tomorrow’s costs affect firm prices today? When a temporary tariff schedule on imported inputs was announced in March 2019, many UK firms adjusted prices in anticipation – despite the potential cost change being in the future. In a recent working paper, we use firm‑level survey data to estimate ‘intertemporal pass‑through’ (IPT): how much expected future marginal costs move current prices. Consistent with modern macroeconomic theory, we find big differences across firms: those that change prices less often, and expect the shock sooner, responded the most. A model shows this variation across firms makes aggregate inflation more forward‑looking, so announcements of future policies can move inflation today.

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