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Abstract This paper shows how the structural representation of a vector autoregressive model can support forecast analysis. We offer a unified framework that formalizes how the structural form of the model can help form a narrative for two key statistics in real-time VAR forecasting: the forecast errors at the outturn of the data, and the consequent revisions of the forecast. We conduct an exercise on the UK, focusing on the inflation surge that started in 2022. We show that revisions to the inflation forecast were driven not only by contractionary supply-side shocks, but also by a mix of expansionary demand-side shocks, with an important role played by revised past shocks. However, we find that the role of demand shocks is subsequently revised downwards as new data vintages become available.