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# Re: st: rolling moments

 From annoporci To statalist@hsphsun2.harvard.edu Subject Re: st: rolling moments Date Sun, 23 Dec 2012 05:57:55 +0800

```I forgot to add, this is my best shot:

```
rolling sd=r(sd) skewness=r(skewness) kurtosis=r(kurtosis), window (60) clear: summarize, detail
```
The problem is that it generates a lot of gaps because of missing data.

On Sat, 22 Dec 2012 23:28:22 +0800, annoporci <annoporci@gmail.com> wrote:

```
```Dear Statalist,

```
I have financial data whose statistical properties I'd like to analyze. I'm new to this so any pointer would be appreciated.
```
I would like to do the following:

```
plot a moving average of the mean/sd/skewness/kurtosis, where either I would use a fixed horizon, e.g. 60 observations (add one drop one as you move with time) or I would use exponentially declining weights on distant observations, for a fixed predetermined weight parameter.
```
```
I have found user-contributed programs to do that for the mean/sd, but not for the skewness and kurtosis. I'm especially interested in the kurtosis. In fact, I would like to also compute semi-kurtosis, e.g. semi-kurtosis for negative returns or for returns below average.
```
```
I could probably work this out on my own by writing down the formulas in terms of the first 4 moments, but I have read exchanges that suggest that efficiency issues may be important, so I'd like to start on the right track, and would thus appreciate your suggestions.
```
```
my dataset has the date and the daily return for different portfolios x1, x2, x3, etc. over a 30 year period, so efficiency in the code is likely to be necessary.
```
many thanks,

Patrick.

my main reference:
http://www.stata.com/statalist/archive/2008-08/msg00153.html
```
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```

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