Below we show how to perform post estimation hypothesis tests on models based
on multiply imputed data with **mi estimate**, **mi test** and **mi testtransform**.

The example for this faq uses data on high school students. The
variables **read**, **write**, and **math** give the student’s
scores in reading, writing, and math respectively. The variable **female**
is equal to one if the student is female and zero otherwise. Finally, **
prog** contains information on the type of program the student is in
either general, academic, and vocational. The multiply imputed datasets are
created using **mi impute ** and are saved into in a single file which contains all 10 imputations
as well as the original data. The variable **_mi_m **gives the imputation number, **_mi_m** = 0
contains the original data.

use http://www.ats.ucla.edu/stat/data/hsbmar, clearmi set mlongmi register imputed female math read science socstmi impute chain (logit) female (regress) math science socst read = ///ses write awards, add(10) force

Below we use **mi estimate:regress** to fit a linear regression model. The **mi estimate: **
prefix informs Stata that we want to analyze multiply imputed
datasets, without it, the command would be performed on the dataset as though it
were a single dataset, rather than a series of multiply imputed
datasets.

mi estimate: regress read write i.female math i.progMultiple-imputation estimates Imputations = 10 Linear regression Number of obs = 199 Average RVI = 0.1481 Largest FMI = 0.2715 Complete DF = 193 DF adjustment: Small sample DF: min = 69.10 avg = 127.30 max = 174.50 Model F test: Equal FMI F( 5, 172.3) = 33.18 Within VCE type: OLS Prob > F = 0.0000 ------------------------------------------------------------------------------ read | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- write | .4059465 .0840575 4.83 0.000 .2395667 .5723263 | female | female | -3.18419 1.296704 -2.46 0.016 -5.763144 -.6052363 math | .3980997 .0898392 4.43 0.000 .21888 .5773194 | prog | academic | 1.467474 1.444345 1.02 0.311 -1.385213 4.320161 vocation | -.7782421 1.586677 -0.49 0.624 -3.90979 2.353306 | _cons | 11.04381 3.939979 2.80 0.006 3.260903 18.82671 ------------------------------------------------------------------------------

Once the model is estimated the **mi** **test** command with the prefix
can be used to perform multiple degree of freedom tests. One common use for this is to
test for an overall effect ofa nominal variable represented by a series of dummy variables.
Below we use **mi test:** to test for an overall effect of type of program (**prog**).

mi test 2.prog 3.prog( 1) 2.prog = 0 ( 2) 3.prog = 0 F( 2, 181.6) = 1.23 Prob > F = 0.2952

The **mi** **test** command can also be used to test nested models, where the null
hypothesis is that the coefficients on two or more variables are simultaneously equal to zero.

mi test math write( 1) math = 0 ( 2) write = 0 F( 2, 132.6) = 52.12 Prob > F = 0.0000

It is also possible to test linear combinations of variables. Below we test a model
with an interaction between **math** and **female**. The variable **female**
is dummy coded (0=male, 1=female). First we create the interaction as we
normally would, then we use the **regress** command with the **mi estimate:** prefix to
fit a regression model.

Then the **mi estimate: **and** mitesttransform **command
can be used to test the null hypothesis that the effect of **math** on **read** is zero when
**female**=1. The **coeflegend **option specifies the legend of coefficients and
how to specify them in an expression. We will need these coefficient names in order to estimate
the effect of **math** for **female**=1.

mi estimate (math_slope_female:_b[math] + _b[1.female#c.math]), coeflegend: /// regress read write i.female##c.math i.progTransformations Average RVI = 0.1536 Largest FMI = 0.1379 Complete DF = 192 DF adjustment: Small sample DF: min = 124.38 avg = 124.38 Within VCE type: OLS max = 124.38 math_slope~e: _b[math] + _b[1.female#c.math] ----------------------------------------------------------------------------------- read | Coef. Legend ------------------+---------------------------------------------------------------- math_slope_female | .4542803 _b[math_slope_female] -----------------------------------------------------------------------------------mi testtransform math_slope_femalemath_slope~e: _b[math] + _b[1.female#c.math] ( 1) math_slope_female = 0 F( 1, 124.4) = 20.67 Prob > F = 0.0000