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RE: st: Missing observations


From   Csaba Kertai <csaba.kertai@hotmail.co.uk>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Missing observations
Date   Thu, 20 Jun 2013 20:44:55 +0200

Thank you Nick. Could you let me know what is not clear about this, please? Let me explain what I want to do in another way. I have 9 variables each having different number of values. These 9 variables are return variables (e.g. 1-year raw return, 2-year raw return etc.) and I need to compare the means/medians/25th/75th/90th percentiles and the percentage of positive values (within one 'group') of these variables to see whether, say, the median difference between the 1-yr raw return 'group' and the 2-yr raw return 'group' is significant. For this, I have to use traditional parametric tests (i.e. the t-test) and non-parametric bootstrapping. 

Could you help me with this, please? I've been scouring the Internet for a solution to testing percentile differences but it seems that there's not much on this particular issue. 
There are basically three things I cannot get my head round: how to test the median difference of 2 'groups' (tried 'signrank' and 'signtest' but these tests are paired tests), the percentiles difference of two 'groups', and the difference of the percentage of positive values between 2 'groups'. 

So you say that one solution could be to stack the 9 variables on top of each other and then group them by, say, inserting a second column (grouping variable) with numbers that will identify the 9 groups?

Thank you  


----------------------------------------
> Subject: Re: st: Missing observations
> From: njcoxstata@gmail.com
> Date: Thu, 20 Jun 2013 18:29:32 +0100
> To: statalist@hsphsun2.harvard.edu
>
> This is really isn't clear to me, but it may be that -var1- and -var2- should be stacked on top of each other.
>
> Nick
> njcoxstata@gmail.com
>
> On 20 Jun 2013, at 15:41, Csaba <csaba.kertai@hotmail.co.uk> wrote:
>
>> Nick,
>>
>> Thank you for your reply. Yes you are right I muddled up observations with values. I meant to write values not observations. My problem is that if I use 'drop if missing(var2)' that will drop values for each variable in my data set.
>>
>> I need to compare the means/medians of 2 variables. Var1 has 1125 non-missing values, var2 has 169 non-missing values. I might be doing sth wrong but when I try using bootstrapping I get a message saying that I should drop any missing values as bootstrapping cannot distinguish between missing and non-missing values. That's why I want to drop missing values for Var2. Basically, I want to achieve the same result as with the unpaired two-sample mean comparison test but with bootstrapping.
>>
>> Thanks a lot!
>>
>> On 20 Jun 2013, at 12:32, Nick Cox <njcoxstata@gmail.com> wrote:
>>
>>> -drop- as used here drops entire observations (outside Stata
>>> observations are known as rows, cases, records). You seem to be under
>>> the impression that there is an operation
>>>
>>> drop missing values
>>>
>>> that is somehow different from
>>>
>>> -drop- observations
>>>
>>> but I don't know what that would look like.
>>>
>>> In your example if -var2- has only 169 non-missing values (_not_
>>> observations) then
>>>
>>> drop if missing(var2)
>>>
>>> will leave precisely 169 observations. I don't understand how that is
>>> a surprise or what else you want.
>>>
>>> Nick
>>> njcoxstata@gmail.com
>>>
>>>
>>> On 20 June 2013 11:17, Csaba Kertai <csaba.kertai@hotmail.co.uk> wrote:
>>>
>>>> I need a bit of help with dropping missing observations. If I use 'drop if missing(var)' or drop if 'var'==. etc. many other observations are dropped as well. More precisely, var1 has 1125 observations and var2 has 169 observations. I want to drop missing observations for var2 but if I use drop if var2==. then this will keep only 169 observations for each variable. I only want to drop values that are missing.
>>>
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