Raking ratio estimation, a version of ipf and a competitor to Merz's
method, is available in Nick Winter's -survwgt- package, downloadable
from SSC.
On Sat, May 16, 2009 at 12:04 PM, Nick Cox <[email protected]> wrote:
> Similar-sounding problems have been attacked through a technique that
> uses a much simpler algorithm and goes under many different names.
>
> See -ipf- and -mstdize- on SSC and any encyclopaedic survey of
> categorical data analysis.
>
> Nick
> [email protected]
>
> Andreas Peichl
>
> I'm interested in re-weighting a microdata sample to fit aggregate
> control
> data based on the Minimum Information Loss (MIL) principle (see Merz
> 1994).
> Based on information theory this principle satisfies the desired
> positivity
> constraint on the weighting factors to be computed. For the consistent
> solution which simultaneously adjusts hierarchical microdata (e.g.
> household
> and personal information), a fast numerical solution by a specific
> modified
> Newton-Raphson (MN) procedure with a global exponential approximation is
> proposed by Merz (1994).
> This procedure involes numerically solving of a set of non-linear
> equations.
>
> I'm aware of
> http://www.stata.com/support/faqs/lang/nl.html.
> I was wondering if it were possible to solve a set of non-linear
> equations
> with Mata (So far, I have't used Mata but I'd like to invest the time to
> learn it if it helps me solving this problem)? If yes, any hints on how
> to
> tackle this problem are highly appreciated.
> Does somebody know any other programs for Stata/Mata that allow
> reweighting
> of microdata?
>
> Merz, Joachim (1994): Microdata Adjustment by the Minimum Information
> Loss
> Principle. Unpublished.
> http://mpra.ub.uni-muenchen.de/7231/
>
>
> --------------------------------------------------------
> Dr. Andreas Peichl
>
> Research Associate
>
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