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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Tracking attrition in a long-shaped dataset |

Date |
Thu, 21 Mar 2013 15:09:40 +0000 |

Yes, that helps considerably. Have a look at FAQ . . . . . . Identifying runs of consecutive observations in panel data . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox and V. Wiggins 8/05 How do I identify runs of consecutive observations in panel data? http://www.stata.com/support/faqs/data-management/ identifying-runs-of-consecutive-observations/ and see how far that gets you. Advice to look at -tsspell- (SSC) is included. (The standard advice in the Statalist FAQ is to look at the FAQs before posting.) Nick On Thu, Mar 21, 2013 at 2:52 PM, Max <maxliving@gmail.com> wrote: > Hi Nick, > > Thanks for the quick response. Let me clarify. Month is a whole > number, representing a time period, so a person might appear in month > =1, month=2, month=4, but not month=3. In that case, he would have > skipped month 3. Thus, using the code in #1 I would code him as having > returned in the first row he appears (month=1), but not in the second > row (month). So yes, month increments by 1, but time marches on > regardless of whether a person appears in that month or not. > > Does that make more sense? If not, do let me know and I'd be happy to > clarify further. > > Max > > On Thu, Mar 21, 2013 at 10:44 AM, Nick Cox <njcoxstata@gmail.com> wrote: >> What your -month- variable is holding is unclear to me. >> >> In terms of your questions >> >> #1. It is evident that >> >> month == month[_n+1] - 1 >> >> is true if and only -month- increases by 1 from one observation to the next. >> >> It's difficult to check that against your word description. Usually >> with panel data, there is a time variable and then all the business is >> centred on what is or is not true at different times. Here you seem to >> be focussing entirely on the time variable. >> >> #2. Understanding this depends on understanding #1, and evidently failed. >> >> If you don't get a better answer, you will need to ask a better question. >> >> Nick >> >> On Thu, Mar 21, 2013 at 2:19 PM, Max <maxliving@gmail.com> wrote: >> >>> I have a long dataset of ID's (people) and months. People enter and leave >>> the dataset at various points, and can skip months. I want to track >>> attrition by doing two things: >>> 1. Create a dummy variable = 1 in time t if the person appears in time t+1, >>> 0 otherwise (but missing for the last month). I think I've solved this one, >>> but am always curious to hear if anyone has any alternate methods that >>> might be better. Here is my solution: >>> >>> bysort ID (month): gen returned = 1 if month == month[_n+1] - 1 >>> >>> 2. And this is where I'm stuck. I want to create a dummy variable = 1 in >>> time t if the person appears in time t+2, regardless of whether they appear >>> in time t+1 or not. I tried: >>> >>> bysort ID (month): gen returned_2month = 1 if (month == month[_n+2] - 2) >>> >>> But that didn't work because someone who, say, appears in months 1 and 3 >>> will not have an entry for month[_n+2]. But they should in fact be coded as >>> a 1. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Tracking attrition in a long-shaped dataset***From:*Max <maxliving@gmail.com>

**Re: st: Tracking attrition in a long-shaped dataset***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Tracking attrition in a long-shaped dataset***From:*Max <maxliving@gmail.com>

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