help probitmiss
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Title

    Efficient Estimator for Probit model with missing data

Syntax
        probitmiss [depvar] [varlistw] [varlistx] [,] [options]


    options               Description
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      numw                 specifies the number of variables with missing
                            data ñ default is numw(1)

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Description

    probitmiss provides efficient parameter estimates for a probit model of [
    depvar] on the variables listed in [varlistw] and [varlistx] when the
    variables listed in [varlistw] have missing values. The command is
    implemented by typing probitmiss followed by [depvar] [varlistw]
    [varlistx]. [varlistw] is the set of explanatory variables for which some
    observations are missing and [varlistx] is the set of explanatory
    variables with no missing data.  The conditions needed for efficiency are
    discussed in more detail in Conniffe and OíNeill (2011)

    probitmiss also reports the results of a Hausman type test for the
    Missing at Random assumption of the estimator.


Example 1

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    . probitmiss riskydum2 rho_new eta eta2 college woman staciv_1 area5_2
        area5_3 area5_4 area5_5, numw(1)

    This estimates a probit model of riskydum2 on rho_new-area5_5 when only
    rho_new contains missing data.

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Example 2

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    . probitmiss riskydum2 rho_new eta college woman staciv_1 area5_2 area5_3
        area5_4 area5_5, numw(3)

    This estimates a probit model of riskydum2 on rho_new-area5_5 when
    rho_new, eta and college all contain missing data.

    ---------------------------------------------------------------------------


Saved Results

    probitmiss saves the following in e():


    Scalars   
      e(N)            number of complete observations
      e(N2)           total sample size(including incomplete observations)
      e(Chi2)         Chi-squared statistic for Hausman test of MAR




    Matrices  
      e(b)            coefficient vector
      e(V)            variance-covariance matrix of the estimator

 References

    Conniffe,D. and D.OíNeill (2011) Efficient Probit Estimation with
    Partially Missing Covariates,î forthcoming in Advances in Econometrics:
    Volume 27, Missing Data Methods editor D. Drukker.