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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Re: Multiple Imputation |

Date |
Mon, 15 Dec 2008 23:16:28 +0000 (GMT) |

--- 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/

**Follow-Ups**:**Re: st: Re: Multiple Imputation***From:*JIBONAYAN RAYCHAUDHURI <jibonayanrc@yahoo.com>

**References**:**Re: st: Re: Multiple Imputation***From:*JIBONAYAN RAYCHAUDHURI <jibonayanrc@yahoo.com>

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