Re: st: Imputing for missing proportions

[Maarten Buis <maartenlbuis@gmail.com>]

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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