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.


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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