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Re: st: Re: Same code, same machine, same data, different results

From   Nick Cox <>
Subject   Re: st: Re: Same code, same machine, same data, different results
Date   Fri, 7 Sep 2012 09:21:01 +0100

Should be

sort id year quarter

assuming that you have or can -generate- a quarter variable.


On Fri, Sep 7, 2012 at 1:47 AM, Mattia Landoni <> wrote:
> Thanks for the jokes and the constructive answers.
> There is no m:m merge, and the same variables are dropped each time. The
> problem was indeed one of sorting, as two of the answers mentioned,
> together with a wrong use of indexing. There was a command such as
> (simplifying)
> sort id year
> generate a_lag = a[_n - 1] if id == id[_n - 1]
> the dataset is quarterly, so it has more than one observation per year.
> Each time I ran the code, the dataset had a slightly different sorting and
> some rows were assigned each time different values.
> On Wed, Sep 5, 2012 at 5:10 PM, Mattia Landoni <> wrote:
>> Dear statalisters,
>> a friend of mine has a bizarre problem. She is running a regression as follows:
>> xi: regress a b c i.d i.e
>> and her output is different every time. Has anyone ever seen a
>> behavior like this? Below are some details.
>> Environment:
>> - Stata 11
>> - Windows 32-bit
>> Precise description:
>> The do-file imports several files from .csv, then merges them, then
>> runs the regression. If I run the do-file, I get certain results. If I
>> issue the same regression command again, I get again the same results,
>> as it should be. However, if I re-run the do-file from the beginning,
>> I get slightly different results and the regression even reports a
>> slightly different number of observations. (Say, 2663 vs. 2666). Every
>> time all the data are taken afresh from the same static .csv sources.
>> There is nothing random about the do-file, that I know. The xi:
>> command generates about 200 i-variables and a few, maybe 10, are
>> dropped because of collinearity. There are more than 2500
>> observations.
>> I could post the do-file here, but it's big and messy. If anyone has
>> any insight after reading the above description, I'd be very glad to
>> hear it.
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