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I'm confused by the concept of the ragged edge in real-time data analyses.

I understand that data for $x_t$ comes in various forms: a first estimate, a series of additional estimates and, after some time, a final value for $x$ in period $t$ which the statistical agency takes as the `truth'.

But this seems to be quite different from what I read about the ragged-edge problem since that seems to suggest that some data series won't be available (missing) in certain periods.

Would appreciate some clarification from anyone.

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The "ragged edge" seems to be more centered around the challenge of so-called "now-casting" -- essentially, near-real-time very-short-term fore- and back-casting -- given that information from different sources is released on different schedules.

It's not really a new issue. For example, GDP and other macro-level data is routinely lagged for developing countries, sometimes by several years. It's these cross-sectional gaps that make the "edge" appear "ragged".

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