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Re: st: Re: Multiple Imputation


From   JIBONAYAN RAYCHAUDHURI <jibonayanrc@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Re: Multiple Imputation
Date   Mon, 15 Dec 2008 15:59:04 -0800 (PST)

Hi,
Thank you for the code. When I run it it gives me the following error:

 ice wage grade age union, clear m(5)
saving() required
r(100);

I have Stata 9.2. What am I doing wrong here?
Jibonayan


--- On Mon, 12/15/08, Maarten buis <maartenbuis@yahoo.co.uk> wrote:

> From: Maarten buis <maartenbuis@yahoo.co.uk>
> Subject: Re: st: Re: Multiple Imputation
> To: statalist@hsphsun2.harvard.edu
> Date: Monday, December 15, 2008, 3:16 PM
> --- JIBONAYAN RAYCHAUDHURI <jibonayanrc@yahoo.com>
> wrote:
> > The problem that I now have is that  I am using  a
> > user written program called levpet that generates
> values of a
> > variable  (Total Factor Productivity) for a firm using
> a non-linear
> > algorithm. This variable is created using data on
> employment, net
> > value added and capital. I have  large number of
> missing values on
> > employment.  If I generate values of the missing
> observations for
> > employment  in my original data set using ice, I get n
> number of
> > imputed data files (where n is the no. of
> imputations). When I load
> > them into memory, I cannot get levpet to work over all
> data sets (at
> > least I do not how to get levpet to generate imputed
> values of tfp
> > over several data sets) to generate the TFP measure.
> Therefore, I am
> > using values of employment imputed from salary and
> wage data.  Given
> > the limitation that I face, what steps can I take to
> ensure that
> > impute does a reasonable job.
> 
> You should not use -impute-, and you don't need to. In
> all likelihood
> you can just use -mim- with the -cat(fit)- option. To
> install -mim-
> type -ssc install mim-.
> 
> If -mim- doesn't work then your first conclusion should
> be that you
> typed something wrong, and should try harder to make -mim-
> work. If it
> really is not possible then you can do this manually, as
> -levpet- 
> allows you to use -if-, so you can estimate the parameter
> of interest
> in each imputed sample by selecting on the variable _mj:
> the first
> sample is _mj==1, the second _mj == 2, etc. After that you
> can combine
> the results using the equations discussed here: 
> http://www.stat.psu.edu/~jls/mifaq.html#howto
> 
> The results of -levpet- seem to be stored in e(b) and e(V)
> just like
> all other regular Stata estimation commands, so the example
> below using
> -regress- can straightforwardly generalized to -levpet-.
> 
> *------------------------- begin example
> -----------------------------
> sysuse nlsw88, clear
> replace wage = . if uniform() < invlogit(5 - .5*grade)
> 
> ice wage grade age union, clear m(5)
> 
> reg wage grade age union if _mj == 1
> matrix b = e(b)'
> matrix v = e(V)
> matrix V = vecdiag(v)'
> reg wage grade age union if _mj == 2
> matrix b = b, e(b)'
> matrix v = e(V)
> matrix V = V, vecdiag(v)'
> reg wage grade age union if _mj == 3
> matrix b = b, e(b)'
> matrix v = e(V)
> matrix V = V, vecdiag(v)'
> reg wage grade age union if _mj == 4
> matrix b = b, e(b)'
> matrix v = e(V)
> matrix V = V, vecdiag(v)'
> reg wage grade age union if _mj == 5
> matrix b = b, e(b)'
> matrix v = e(V)
> matrix V = V, vecdiag(v)'
> 
> mata:
> b = st_matrix("b")'
> V = st_matrix("V")'
> Qbar = mean(b)'
> Ubar = mean(V)'
> B = diagonal(variance(b))
> T = Ubar :+ 1.2:*B
> se = sqrt(T)
> df= 4:* (1 :+ (5:*Ubar):/(6:*B)) :* (1 :+
> (5:*Ubar):/(6:*B))
> t = Qbar:/se
> p = 2*ttail(df, abs(t))
> ci = Qbar :- invttail(df,0.025):*se, Qbar :+
> invttail(df,0.025):*se
> result = Qbar, sd, t, df, p, ci
> st_matrix("result", result)
> end
> 
> matrix rownames result = grade age union _cons
> matrix colnames result = coef std_err t df p lb ub
> matrix list result
> *--------------------------- end example
> --------------------------
> (For more on how to use examples I sent to the Statalist,
> see
> http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html )
> 
> >  Would reporting the correlations between salaries and
> employment for
> > non-missing observations help? Is there any method for
> setting a
> > bound on the prediction error that is unaccounted in
> the impute
> > command? 
> 
> You can simulate (my solution to almost everything), but
> that is a
> waste of time, as you can and should use -ice- instead.
> 
> Hope this helps,
> Maarten
> 
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
> 
> visiting address:
> Buitenveldertselaan 3 (Metropolitan), room N515
> 
> +31 20 5986715
> 
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
> 
> 
>       
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