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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/ > ----------------------------------------- > > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Re: Multiple Imputation***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**Re: st: Re: Multiple Imputation***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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