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Re: st: Is there a way to use or emulate the behaviour of --predict-- with --by-- groups?


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Is there a way to use or emulate the behaviour of --predict-- with --by-- groups?
Date   Wed, 31 Oct 2012 18:18:18 +0000

Did you think of interpolating the logs and then exponentiating?

Regression of any flavour can't interpolate unless all the residuals
are zero; it can only smooth. If that's what you want, fine, but it is
not like -ipolate-.

Nick

On Wed, Oct 31, 2012 at 5:49 PM, Aaron Kirkman <ak1795mailserv@gmail.com> wrote:
> Thank you Maarten. The interaction term does exactly what I need. Once
> I added --set matsize 3000-- to my actual data set, the interpolation
> works well.
>
> On Wed, Oct 31, 2012 at 3:19 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:
>> On Wed, Oct 31, 2012 at 1:40 AM, Aaron Kirkman wrote:
>>> I have data grouped by a variable called --group--, in this example,
>>> and I'm trying to use logarithmic interpolation on another variable.
>>> Linear interpolation using the --ipolate-- command works perfectly in
>>> --by-- group <snip> Unfortunately, this does not work with logarithmic
>>> interpolation and --regress--/--predict--.
>>
>> You can avoid the -by:- prefix by adding interactions to your linear
>> regression model:
>>
>> *---------- begin example ----------
>> clear
>> quietly input str1 group x y
>> A 1 1000
>> A 2 .
>> A 3 3000
>> A 4 .
>> B 5 45
>> B 6 .
>> B 9 20
>> end
>>
>> encode group, gen(groupnum)
>> gen lx = ln(x)
>>
>> reg y i.groupnum##c.lx
>>
>> predict y_loginterp, xb
>> *----------- end example -----------
>>
>> Notice that the observation A 4 is not strictly speaking an
>> interpolation but an extrapolation. You'll want to be more careful in
>> those situations.
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