Here is Matt's code with a couple of macros to reduce typing. I've
subtracted 1 from the row mean and also subtracted 3 from the
kurtosis formula Matt used. See: http://www.itl.nist.gov/div898/
handbook/eda/section3/eda35b.htm
-Steve
**************************CODE BEGINS**************************
sysuse auto,clear
****************************************************
* Create local macro vlist with variables to analyze
****************************************************
local vlist "mpg rep78 trunk turn"
egen rowmean = rowmean(`vlist')
egen rowN =rownonmiss(`vlist')
forvalues i=2/4{
gen m`i'=0
foreach v of local vlist {
replace m`i' = m`i' + (`v'-rowmean)^`i'
di m`i'
}
replace m`i' = m`i'/(rowN-1)
}
** http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm **
gen rowskew = m3*m2^(-3/2)
gen rowkurt = m4*m2^(-2) -3
list `vlist' row* in 1/5
***************************CODE ENDS*******************************
On Oct 13, 2008, at 11:21 PM, Matt Spittal wrote:
jeheyman,
You can use the moments about the mean to calculate skew and
kurtosis for a row of variables. Imagine that you want to do this
for the variables weight, length and price from the auto dataset.
sysuse auto, clear
// get mean and N
egen rowmean = rowmean(weight length price)
egen rowN = rownonmiss(weight length price)
// calculate 2, 3, and 4th moments about the mean
gen m2 = 1/rowN * ((weight - rowmean)^2 + (length - rowmean)^2 +
(price - rowmean)^2)
gen m3 = 1/rowN * ((weight - rowmean)^3 + (length - rowmean)^3 +
(price - rowmean)^3)
gen m4 = 1/rowN * ((weight - rowmean)^4 + (length - rowmean)^4 +
(price - rowmean)^4)
// calculate skew and kurtosis
gen rowskew = m3*m2^(-3/2)
gen rowkurt = m4*m2^(-2)
list weight length price rowskew rowkurt in 1/10
-- Matt
[email protected]
-----Original Message-----
From: [email protected]
[mailto:[email protected]]On Behalf Of jeheyman
Sent: Tuesday, 14 October 2008 12:31 PM
To: [email protected]
Subject: st: rowskew?
Is it possible to calculate essentially a rowskew and rowkurtosis in
the same way that egen calculates rowmean?
For each observation I have 18 variables and I need, obviously, the
three distribution measures. Mean is trivial but the other two are
proving elusive. I feel like I'm overlooking something obvious and
posting seemed to be better than continuing to bang my head against
the wall.
Thanks.
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