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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Computing local variance |

Date |
Sun, 22 Feb 2009 18:30:26 -0000 |

With just one trick, creating a pseudo-time variable, you can exploit the existence of -mvsumm- from SSC, which codes this, and indeed a more general approach. -mvsumm- is not blisteringly fast -- it predates Mata -- but it should save almost all your programming time. Nick n.j.cox@durham.ac.uk Benjamin Villena Roldan I have two continuous variables X and Y. I'm trying to do the following: 1. Sort the data using X 2. For each observation of X, I compute the local variance of Y by a nearest neighborhood approach. I take the 2k closest observations to an observation X[i], i.e. using observations between X[i-k+1] and X[i+k]. 3. I'm implementing this approach by using a forvalue loop such as sort X count if X!=. local k=ceil(r(N)^0.5/2) local K=r(N)-`k' gen SD_Y=. forv i=`k'/`K' { local k0=`i'-`k'+1 local k1=`i'+`k' qui summ Y in `k0'/`k1' replace SD_Y=r(sd) in `i'/`i' } So, I have two questions/problems about this code 1. I need to do the same procedure several times and it is very time-consuming. Is there a way to speed up the execution? How much time would I gain if I implement a similar code in C++? 2. There are missing observations in X and Y, how can I restrict the sort command to deal with nonmissing values of both variables. A simple answer is to do -keep if X!=. & Y!=. Can I do it without dropping data? * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: RE: RE: Computing local variance***From:*"Benjamin Villena Roldan" <bvillena@troi.cc.rochester.edu>

**References**:**st: Quantile regression with stata***From:*Tomas M <anon556656@live.ca>

**st: RE: Quantile regression with stata***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**RE: st: RE: Quantile regression with stata***From:*Tomas M <anon556656@live.ca>

**st: Computing local variance***From:*"Benjamin Villena Roldan" <bvillena@troi.cc.rochester.edu>

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