On reshaping the data and running mi impute mvn as you suggested I get the error message newyear not found...but newyear which is the time subscript gets dropped when reshaping. Why does mvn given this error message??
--- On Fri, 8/7/09, Yulia Marchenko, StataCorp LP <[email protected]> wrote:
> From: Yulia Marchenko, StataCorp LP <[email protected]>
> Subject: Re: st: mi in Stata 11
> To: [email protected]
> Date: Friday, August 7, 2009, 11:54 AM
> JIBONAYAN RAYCHAUDHURI <[email protected]>
> asks if Stata 11's -mi- command
> provides imputation methods for panel data:
>
> > Can mi in Stata 11.0 perform imputation over panel
> data (which has been
> > tsset)? Does the data need to be arranged in wide form
> (from long form)
> > before mi can be applied to the data set?
>
> -mi- does not provide imputation methods specifically
> designed to impute
> complex data, such as panel, longitudinal data, complex
> survey data,
> time-series data, etc. The methods employed by -mi-
> rely on the iid
> assumption which is violated in these data, and to the best
> of my knowledge
> the methodologies for imputation methods relaxing this
> assumption have yet to
> be fully developed.
>
> In some cases, there are ways of using existing iid
> imputation methods to
> impute complex data. For example, longitudinal data
> can be reshaped to wide
> form (one variable for each time period) and then the MVN
> model can be used
> for imputation. In Stata, you can use -mi impute mvn-
> to do that. Say we
> have subjects' weights measured at three time periods: y1,
> y2, y3. We can
> type
>
> . mi register imputed y1 y2 y3 //
> register variables to be imputed
> . mi impute mvn y1 y2 y3, add(10) //
> create 10 imputations
>
> If you now want to reshape your data to long form after
> imputation, you can
> use -mi reshape-:
>
> . mi reshape y, i(id) j(time)
>
> where variable 'id' contains observation identifiers and
> new variable 'time'
> will contain the time periods after the data are
> reshaped. Note that you
> should use -mi reshape- rather than -reshape- to reshape
> _mi_ data.
> Similarly, if your data are in long form, you can use -mi
> reshape- to reshape
> it to wide form prior to using -mi impute-.
>
> In the presence of clustering, stratification, missing data
> can be imputed
> conditionally on the design variables, provided there are
> not too many
> clusters or strata. For example, if continuous
> variables x1 and x2 contain
> missing values and data are stratified on race, you can
> account for
> stratification by including variable 'race' as a factor
> variable in the
> imputation model:
>
> . mi register imputed x1 x2
> . mi impute mvn x1 x2 = i.race, add(5)
>
> In the above, I could have also used -mi impute monotone-,
> instead, if I knew
> that the pattern of missing data is monotone.
>
> See Rubin (1987), Schafer (1997, 29-35, 372-377), for
> example, for more
> information about imputing complex data.
>
> Jibonayan mentions the use of -tsset- which implies that
> the data are also
> time-series data. I'm not aware of imputation methods
> applicable to filling
> in time-series data.
>
> In reply to Jibonayan's question, Martin Weiss <[email protected]>
> points
> out:
>
> > There is an -mi tsset- command as seen in which makes
> me think there is
> > support for imputation for panel data...
>
> Although -mi- does not provide direct methods for imputing
> panel data,
> time-series data, etc., it provides ways of 'mi setting'
> such data in case
> users already have imputations for it from other sources
> and need to perform
> data manipulation.
>
> Jibonayan also asks if a user-written command -levpet- can
> be used with -mi-:
>
> > Is it possible to combine mi with the levpet method of
> generating TFP?,i.e.,
> > can levpet be applied over imputed data sets and the
> overall TFP measures,
> > thus generated, combined?
>
> Technically, you can use -mi estimate- with -levpet- (or
> with any other
> estimation command outside the list of supported commands
> in -help mi
> estimation-) to obtain combined estimates of the
> coefficients if you specify
> -mi estimate-'s option -cmdok-:
>
> . mi estimate, cmdok: levpet ...
>
> Statistically, it is your responsibility to verify that
> multiple imputation
> (MI) is applicable for the estimation method used. In
> general, as long as
> approximate (asymptotic) normality holds for an estimator
> and the variance of
> the estimator is a consistent estimate of the true
> variability in the complete
> data, it should be ok to apply MI combination rules to this
> estimator.
>
> Now, what Jibonayan really wants are the combined estimates
> of the predictions
> after using -mi estimate- with -levpet- which -mi- does not
> provide. There is
> no definitive recommendation on how predictions must be
> handled within the MI
> framework. Jibonayan may want to check out
> user-written command -mim- (and
> -mim: predict-, in particular) for a way of obtaining
> predicted values with
> multiply-imputed data.
>
> In any case, Jibonayan should first decide on what would be
> an appropriate way
> of imputing the time-series data before performing the
> analysis.
>
>
> References:
>
> Rubin, D. B. 1987. Multiple Imputation for Nonresponse in
> Surveys. New York:
> Wiley.
>
> Schafer, J. L. 1997. Analysis of Incomplete Multivariate
> Data. Boca Raton,
> FL: Chapman & Hall/CRC.
>
>
> -- Yulia
> [email protected]
> *
> * For searches and help try:
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> * http://www.stata.com/support/statalist/faq
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>
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