Vora wrote:
> If I do:
>
> [1]
> .kdensity income
>
> Then the default kernal is Epanechnikov
> and the default bandwidth is the "optimal"
> bandwidth (silverman).
>
> ----------------------
> If I do:
>
> [2]
> .kdens income
>
> Then the default kernal is Epan2
> and the default bandwidth is silverman.
> [In your paper, equation 28 and equation 29
> are actually the same thing?]
>
> ----------------------
> So if I want kdens to provide the same graph
> as the default kdensity then I should do:
>
> [3]
> .kdens income, k(e)
>
> ----------------------
[Formula 28 is a special case of formula 29. Formula
28 is formula 29 for the gaussian kernel.]
Graphs [1] and [3] should look different - but not too different. There
are several differences between -kdensity- and -kdens-. Among them are:
1. -kdens- uses binned data whereas -kdensity- uses the raw data. This
causes slight differences in the estimates.
2. -kdens- uses a version of the "optimal of Silverman" that takes into
account the canonical bandwidth of the kernel function. -kdensity- does
not do this adjustment (it always uses a gaussian scaling). Gaussian and
epanechnikov have different canonical bandwidths, so the bandwidth
estimates used in [1] and [3] will differ (not very much, though).
Here's an example:
. sysuse auto, clear
(1978 Automobile Data)
. kdensity price, nograph
. di r(width)
605.64238
. kdens price, k(epanechnikov) nograph
(n2() set to 74)
. di r(width)
599.61225
. kdens price, k(gaussian) nograph
(n2() set to 74)
. di r(width)
605.64238
3. The default in -kdens- is to return 512 estimation points (or _N if
_N<512). -kdensity- returns 50.
4. The range of estimation points is set differently.
If you feel that these points do not explain the differences in your
graphs then maybe do something like
. kdensity income, gen(at d1) nograph
. kdens income, k(e) bw(`r(width)') at(at) gen(d2) nograph
. line d1 d2 at
Do the two estimates still look different?
ben
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