数据补漏

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stata是否可有通过编程，对于不规则区域数据的缺失，按照一定规则进行一次性补漏呢？
听过一些补漏，但是是对于有一定规则遗漏的数据进行填补，但是对于比如面板数据中不规则的小部分数据的遗漏，怎样补漏呢？

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关键词：不规则区域 面板数据 区域数据 不规则 一次性 stata编程 数据补漏

你若要问程序，永远附上相关资料；若附上资料，永远用 dataex 印出资料。先 ssc install dataex (并见说明)，将原始 Stata 资料中具有”代表性”的一部分资料列出，以供有意回答者实验之用，并能提供具体操作指令。并请参考 http://www.jianshu.com/p/9870080fe769,  https://bbs.pinggu.org/thread-5048204-1-1.html, 与 https://bbs.pinggu.org/thread-5917273-1-1.html。

我搞不懂，一个为所有世界顶尖 Stata 用者共同会做的建议，而且几乎所有人都会遵守此建议，怎么在这个论坛就变成"灌水"了，请警告者看看 https://www.statalist.org/forums/help， 特别是

1. 12.2 What to say about your data
   
2. 
   
3. We can understand your dataset only to the extent that you explain it clearly.
   
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5. The best way to explain it is to show an example. The community-contributed command dataex makes it easy to give simple example datasets in postings. It was written to support Statalist and its use is strongly recommended. Usually a copy of 20 or so observations from your dataset is enough to show your problem. See help dataex for details.
   
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7. As from Stata 15.1 (and 14.2 from 19 December 2017), dataex is included with the official Stata distribution. Users of Stata 15 (or 14) must update to benefit from this. Users of Stata 16 or 17 need not do anything: dataex is already part of your installation.
   
8. 
   
9. Users of earlier versions of Stata must install dataex from SSC before they can use it. Type ssc install dataex in your Stata.
   
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11. The merits of dataex are that we see your data as you do in your Stata. We see whether variables are numeric or string, whether you have value labels defined and what is a consequence of a particular display format. This is especially important if you have date variables. We can copy and paste easily into our own Stata to work with your data.
    
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13. If your dataset is confidential, then provide a fake example instead.
    
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15. The second best way to explain your situation is to use one of Stata's own datasets and adapt it to your problem. Examples are the auto data and the Grunfeld data (a simple panel dataset). That may be more work for you and you may not find an analog of your problem with such a dataset.
    
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17. The worst way to explain your situation is to describe your data vaguely without a concrete example. Note that it doesn't help us much even to be given your variable names. Often that leaves unclear both your data structure and whether variables are numeric or string or their exact contents. If you explain only vaguely, quick answers to your question, or even any answers at all, are less likely.

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