<p>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.</p>

<p>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".</p>

CC BY-SA 4.0
2019-11-26T22:49:08.937Z
2019-11-26T22:49:08.937Z
heh
<p>I'm confused by the concept of the ragged edge in real-time data analyses. </p>

<p>I understand that data for <span class="math-container">$x_t$</span> comes in various forms: a first estimate, a series of additional estimates and, after some time, a final value for <span class="math-container">$x$</span> in period <span class="math-container">$t$</span> which the statistical agency takes as the `truth'. </p>

<p>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. </p>

<p>Would appreciate some clarification from anyone.</p>

<econometrics><data>
ragged edge concept
user25016
# ragged edge concept

(Asked by: username_0 on 2019-11-26)

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.

**Tags:** econometrics, data

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## 1 answers

### Answer by username_1 on 2019-11-26. Score: 1

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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