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Standard vector autoregression (VAR) identification methods find that government spending raises consumption and real wages; the Ramey–Shapiro narrative approach finds the opposite. I show that a key difference in the approaches is the timing. Both professional forecasts and the narrative approach shocks Granger-cause the VAR shocks, implying that these shocks are missing the timing of the news. Motivated by the importance of measuring anticipations, I use a narrative method to construct richer government spending news variables from 1939 to 2008. The implied government spending multipliers range from 0.6 to 1.2.