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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Multivariate kernel regression |

Date |
Wed, 17 Oct 2012 14:25:47 -0400 |

Josh Hyman <hyman.josh@gmail.com>: Taking the mean of Y for values of X near X0 *is* a regression; you are calculating the conditional mean of Y. What you describe is a zero-degree local polynomial regression in -lpoly- (a regression on just a constant), which is inadvisable (though -lpoly- default behavior) for the reasons given in the -lpoly- manual entry. Better to regress on X and interactions (all in deviation form from point X0) and predict at X=X0. I recommend you start with a simple example with say 100 values of a one-dimensional X and try calculating the means of Y at (say) 10 values using a couple different approaches, to get a sense of what you are doing. Then generalize to 100*100 values of X1 and X2 and calculate mean Y at (say) 100 points on that grid. Did you look at http://fmwww.bc.edu/repec/bocode/t/tddens (multivariate kernel density estimation)? Ask John DiNardo if you have conceptual questions--if he is currently accessible to you at the Ford school--the big ideas may easier to explain in person. On Wed, Oct 17, 2012 at 1:04 PM, Josh Hyman <hyman.josh@gmail.com> wrote: > 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. * * 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:*Josh Hyman <hyman.josh@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>

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

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