Zero-Shot Conditional Forecasting and the Information Content of Central Bank Paths

Central banks publish projection paths, but these need not be optimal summaries of the information used to form them. We reframe conditional macroeconomic forecasting as an input problem: a fixed, pre-trained multivariate foundation time-series model reads the institution's historical predictions and announced future paths alongside target histories, rather than imposing the path as a model-consistent restriction inside a locally estimated system. The mapping nests the Mincer-Zarnowitz and Granger-Ramanathan regressions as its parametric-linear special case. Reading just Norges Bank's published path triple and target histories, the map cuts mean squared error against the Bank on inflation across horizons, and the nested combination regression puts the conditional weight on the map, not the path. A hard-conditioned VAR matched on the same future paths, but blind to the prediction record, is consistently outperformed on the rate and inflation, and the result survives the asymmetric-loss specifications that best rationalise the path. The VAR contrast replicates on Sweden and New Zealand; the institutional comparison only on New Zealand, with parity at best on Sweden. An institutional-learning regression rationalises the split: the Riksbank absorbs its recent misses more aggressively than Norges Bank and the RBNZ, leaving less residual signal to extract.