Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Josh Hyman <hyman.josh@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Multivariate kernel regression |

Date |
Wed, 17 Oct 2012 13:04:04 -0400 |

Hi Austin (and others), Thank you very much for your reply. Sorry about my delayed response - I wanted to investigate more to make sure I understood your suggestion. I'm not sure your suggestion gets me exactly what I was looking for, and I want to clarify. My reference to -lpoly- in my initial post may have been confusing. I don't actually want to do kernel-weighted local regressions. I want to estimate "multivariate kernel regression", which to my understanding, doesn't actually involve any regressions at all. It takes the weighted average of Y for all observations near to the particular value of X, weighted using the kernel function. And where X represents more than 2 variables. So, this actually seems the same to me as multivariate kernel density estimation, which I also don't see any user-written commands for in Stata. What I am looking for, I guess is like a version of -kdens2- that allows for more than one "xvar", and wouldn't output a graph (since it would be in greater than 3 dimensions), but rather would output the fitted or predicted values of the Y (like -predict, xb-) for each observation. Regardless, it sounds like given your suggestion, one way to do this is to loop over all possible combinations of the values of the X variables and calculate the weighted Y for each combination using the kernel of my choice? Please let me know if this would be your suggestion, or if given my further clarification, if you know of any user-written commands in Stata to do this, or if you have any other suggestions. Thanks a lot for your help, and sorry again for the delayed response. Josh On Fri, Oct 12, 2012 at 3:31 PM, Austin Nichols <austinnichols@gmail.com> wrote: > Josh Hyman <hyman.josh@gmail.com>: > If you know the multivariate kernel you want to use, and the grid you > want to smooth over, it is straightforward to loop over the grid and > compute the regressions. To program a general estimator for a wide > class of kernels would be substantially more work. See e.g. -kdens- > on SSC and > http://fmwww.bc.edu/repec/bocode/m/mf_mm_kern > http://fmwww.bc.edu/RePEc/bocode/k/kdens.pdf > > A simple conic (triangle) kernel in 2 dimensions is easiest, see e.g. > http://fmwww.bc.edu/repec/bocode/t/tddens > > On Fri, Oct 12, 2012 at 1:49 PM, Josh Hyman <hyman.josh@gmail.com> wrote: >> Dear Statalist users, >> >> I am trying to figure out if there is a way in Stata to perform >> multivariate kernel regression. I have investigated online and on the >> Statalist, but with no success. What I am looking for would be similar >> conceptually to the -lpoly- command, but with the ability to enter more >> than one "xvar". >> >> If there are no Stata commands to do this (user-written or otherwise), then >> do you recommend coding up a program to do this manually? I have used Stata >> for many years, and written programs before, but have never had to code up >> a regression manually. If you have suggestions on how to do this, or >> resources to consult, that would be greatly appreciated. >> >> Please let me know if I can provide any other information. Thank you for >> your consideration, >> Josh Hyman >> Economics doctoral candidate >> University of Michigan > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Multivariate kernel regression***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: Multivariate kernel regression***From:*Shan Zhizhong <victor.shan@gmail.com>

**References**:**st: Multivariate kernel regression***From:*Josh Hyman <hyman.josh@gmail.com>

**Re: st: Multivariate kernel regression***From:*Austin Nichols <austinnichols@gmail.com>

- Prev by Date:
**st: Suppressing some standard deviations in -esttab-** - Next by Date:
**Re: st: Multivariate kernel regression** - Previous by thread:
**Re: st: Multivariate kernel regression** - Next by thread:
**Re: st: Multivariate kernel regression** - Index(es):