*! Version 10 june2011

help probitmiss

Title

Efficient Estimator for Probit model with missing data

Syntax

probitmiss
[depvar]
[w]
[x]
[,]
[options]

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

Description

probitmiss provides efficient parameter estimates for a probit model of [depvar] on the variables listed in [w] and [x] when the variables listed in [w] have missing values. The command is implemented by typing probitmiss followed by [depvar] [w] [x]. [w] is the set of explanatory variables for which some observations are missing and [x] 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

. 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.

Example 2

. 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.