Thanks for the further suggestion using -levelsof- ; I will go through it
tonight.
Based on the output produced I have now two types of variables:
(1) R* for each variable containing the mean return per decile
(2) G* for each variable containing the decile number 1 to 10
Basically I would like to produce a table like this (where the figures in
the table represent the mean returns of the deciles per variable):
1 2 3 ... 10
Var1 1.2 1.5 1.6 ... 2.3
Var2 0.9 0.7 0.6 ... 0.3
Varx
...
Varn
But somehow don't arrive at summarizing the data in a convenient way,
obviously this (below) does not work as after collapse all other variables
are gone.
foreach X of varlist c1* {
sort G_`X'
collapse (mean) RG_`X', by(G_`X')
}
Please excuse if this is very basic stuff, but I would appreciate a short
hint. Thanks.
- Tom
-----Urspr�ngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Jeph Herrin
Gesendet: Freitag, 10. November 2006 14:28
An: [email protected]
Betreff: Re: AW: st: RE: Decile sorts
So, using -levelsof- per Philipp's suggestion:
levelsof yrm, level(l)
foreach X of varlist c1* {
gen dec_`X'=.
foreach YRM in `l' {
xtile deciles=`X' if yrm==`YRM', n(10)
replace dec_`X'=deciles if yrm==`YRM'
drop deciles
}
bys dec_`X': egen Rr`X'=mean(c1ds_ri)
}
maybe?
jeph
Thomas Erdmann wrote:
> A further note on Jeph's suggestion:
>
> It looks very convenient, but I need to adjust for the fact that I do not
> need the mean of the same item but of a different attribute:
>
> foreach X of varlist c1* {
> xtile deciles_`X'=`X', n(10)
> bysort deciles_`X': egen Rr`X'=mean(c1ds_ri)
> }
>
> But a problem still remains:
> the deciles are calculated over all observations - but what I need is
> calculating the mean of deciles by yrm (my time variable representing
> YearMonth) and afterwards the mean of all deciles groups (1-10) over all
> yrm's. I was not able to integrate this into this short solution as -by-
is
> not allowed for -xtile- .
>
> -Tom
>
>
>
>
>
> -----Urspr�ngliche Nachricht-----
> Von: [email protected]
> [mailto:[email protected]] Im Auftrag von Jeph Herrin
> Gesendet: Freitag, 10. November 2006 01:26
> An: [email protected]
> Betreff: Re: st: RE: Decile sorts
>
> Oops, don't forget to drop -deciles-
>
> foreach X of varlist c1* {
> xtile deciles=`X', n(10)
> bys deciles: egen R`X'=mean(`X')
> drop deciles
> }
>
>
>
>
>
>
> Jeph Herrin wrote:
>> Maybe I'm missing something, but why not:
>>
>> foreach X of varlist c1* {
>> xtile deciles=`X', n(10)
>> bys deciles: egen R`X'=mean(`X')
>> }
>>
>> ?
>>
>> hth,
>> Jeph
>>
>>
>> Nick Cox wrote:
>>> Various comments sprinkled here and there. You may have
>>> strong reasons to use these decile bins, but binning strikes me as,
>>> usually, at best a means towards an end (or perhaps ends towards some
>>> means). Some nonparametric
>>> regression might do more justice to the data.
>>> Also, you are mixing two naming conventions 1...10 and 10...90. Just
>>> use one.
>>> Nick [email protected]
>>> Thomas Erdmann
>>>
>>>> I am trying to sort my observations into deciles according to one
>>>> attribute
>>>> and afterwards calculating the average of another attribute of those
>>>> ten groups.
>>>
>>>> Please find the code I came up with below [lines with ... are
>>>> omitted], yrm is the time variable (YearMonth)
>>>>
>>>> (1) As far as I can tell it works out, but a) it's a lot of code and
>>>> b)produces a lot of variables and c)generating the output is rather
>>>> awkward.
>>>>
>>>> Could you give me hints on how to implement a smarter solution or if
>>>> there
>>>> are any errors in the way the calculation is carried out currently?
>>>
>>>> *** Generate Percentiles
>>>> sort yrm
>>>> foreach X of varlist c1* {
>>>> by yrm: egen p10_`X'= pctile(`X'), p(10.0)
>>>> by yrm: egen p20_`X'= pctile(`X'), p(20.0)
>>>> by yrm: egen p30_`X'= pctile(`X'), p(30.0)
>>>> ...
>>>> by yrm: egen p90_`X'= pctile(`X'), p(90.0)
>>>> }
>>> This is two loops rolled out into one.
>>> sort yrm foreach X of varlist c1* { forval i =
>>> 10(10)90 { by yrm : egen p`i'_`X' = pctile(`X'), p(`i')
>>> }
>>> }
>>>
>>>> *** Sort into Percentile groups
>>>> foreach X of varlist c1* {
>>>> gen G_`X'=1 if `X'<p10_`X' & `X'~=.
>>>> replace G_`X'=2 if `X'>p10_`X' & `X'<p20_`X' ... replace
>>>> G_`X'=9 if `X'>p80_`X' & `X'<p90_`X' replace G_`X'=10 if
>>>> `X'>p90_`X' & `X'~=.
>>>> }
>>> Similar story with boundary conditions.
>>> foreach X of varlist c1* {
>>> gen byte G_`X' = `X' < p10_`X'
>>> forval i = 2/9 { local j = 10 * `i'
>>> replace G_`X' = `i' if `X' < p`j'_`X' & G_`X' == 0 }
>>> replace G_`X' = cond(`X' == ., ., 10) if G_`X' == 0 }
>>>
>>>
>>>> *** Calculate return mean for each group
>>>> sort yrm
>>>> foreach X of varlist G* {
>>>> by yrm: egen R1`X'= mean(c1ds_ri) if `X'==1
>>>> by yrm: egen R2`X'= mean(c1ds_ri) if `X'==2
>>>> ...
>>>> by yrm: egen R9`X'= mean(c1ds_ri) if `X'==9
>>>> by yrm: egen R10`X'= mean(c1ds_ri) if `X'==10
>>>> }
>>> Why do you need all these variables? The results for bin are disjoint,
>>> so can be put in a single variable.
>>> foreach X of varlist G* { bysort yrm `X' : egen R`X' =
>>> mean(c1ds_ri)
>>> }
>>> Having said that, it can probably done more directly with a series of
>>> -collapse-s.
>>> *
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>>>
>>>
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