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Re: st: Imputing for missing proportions


From   Geomina Turlea <geomina@yahoo.fr>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Imputing for missing proportions
Date   Fri, 12 Apr 2013 11:08:32 +0100 (BST)

Maarten, 
Thank you very much for your answer. 
The problem with -mi impute - is that it does not really have an option for regressing proportions. I can't really use truncated regression, and my dependent variable is not binary or categorial, but a continous variable betwen 0 and 1.
I am considering to simulate the multiple imputation with a beta regression for estimation of the missing values.
Very gratefull for an yes/no opinion on this,
Geomina  


--- On Thu, 4/11/13, Maarten Buis <maartenlbuis@gmail.com> wrote:

> From: Maarten Buis <maartenlbuis@gmail.com>
> Subject: Re: st: Imputing for missing proportions
> To: statalist@hsphsun2.harvard.edu
> Date: Thursday, April 11, 2013, 6:28 PM
> On Thu, Apr 11, 2013 at 4:21 PM,
> Geomina Turlea wrote:
> > I am fighting for a while with estimate missing data
> for the share of ICT professionals/total employment, in 59
> industries, 27 EU countries and for 14 years.
> > This data exists in the European Labour Force Survey,
> but the dataset is incomplete.
> >
> > 1. Can I use mi impute with proportions?
> > 2. I used betafit to fit a distribution with values
> between 0 and 1. Than I imputed the missing values from the
> estimated beta distribution. Is this method
> superior/inferior to using mi impute?
> > 3. I tried to use the Kolmogorov-Smirnov test, but I
> don't know what I got wrong. Below is a sequence where I
> created a variable with the distribution beta and then test
> the hypothesis with the K-S test. The test rejects the null
> hypothesis that the data has the distribution I used to
> create it. How could that be?
> >
> > . gen x=rbeta(0.05, 1.77)
> > . ksmirnov x=rbeta(0.05, 1.77)
> 
> My first step would be to look at the industries with
> missing values.
> Sometimes missing just means 0 or negligable, and looking at
> the
> industries would give you a fair guess of whether that is
> the case. If
> that is the case your imputation problem reduces to just a
> recoding
> problem.
> 
> For questions 2 and 3: If you have an imputation problem,
> then you
> should use -mi- and not -betafit- (available from SSC),
> because that
> is what -mi- was designed for.
> 
> For question 3: -rbeta()- gives you random numbers from a
> beta
> distribution, so that is definately not something you want
> to feed in
> -ksmirnov-. I just would use either -margdistfit- or
> -hangroot- (also
> available from SSC) after -betafit- to check the fit.
> 
> -- Maarten
> 
> ---------------------------------
> Maarten L. Buis
> WZB
> Reichpietschufer 50
> 10785 Berlin
> Germany
> 
> http://www.maartenbuis.nl
> ---------------------------------
> 
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