Analysis of incomplete data using multiple imputation in Stata

This replicates MI Example 1 of Allison (2002, pp. 41-50) using Stata 14.

version 14
use "https://statisticalhorizons.com/wp-content/uploads/college.dta", clear

mi set mlong
mi register imputed gradrat csat private lenroll stufac rmbrd act
mi impute mvn gradrat csat private lenroll stufac rmbrd act, ///
   add(5) burnin(500) burnbetween(200) emlog emoutput ///
   saveptrace(trace, replace)

preserve
mi ptrace describe trace 
mi ptrace use trace, clear

// Generate regression coefficient
generate b = v_y2y1 / v_y2y2 

// Figure 5.1
twoway line b iter if inrange(iter, 1, 100), ///
       xtitle(Iteration) ytitle(b(csat)) ///
       ylabel(, format(%6.3f)) name(figure51, replace)


// Figure 5.2
tsset iter
ac v_y2y1, lags(100) ciopts(color(white)) note("") name(figure52, replace)

restore


// Table 5.3
eststo clear
foreach i of numlist 1/5 {
 qui eststo: regress gradrat csat lenroll private stufac rmbrd if _mi_m == `i'
}
esttab, not se wide nostar noobs order(_cons) varlabel(_cons "Intercept") nomtitle


// Figure 5.3
mi estimate: regress gradrat csat lenroll private stufac rmbrd

Reference

Allison, Paul D. 2002. Missing Data. Sage. doi: 10.4135/9781412985079

Multiple imputation in Stata

clear

// Generate data
set obs 10000
generate e  = rnormal()
drawnorm x1 x2, ///
         corr(1, .4, 1) cstorage(lower) 
generate y = 1 + 1 * x1 + 1 * x2 + 25 * e
drop e

// Model with data-generating process
eststo clear
eststo: regress y x1 x2

// Drop cases conditional on other covariate
replace x1 = . if rnormal() > .1 & x2 > -1

// Model with missing data
eststo: regress y x1 x2

// Impute data
mi set mlong                  // Declare data format
mi register imputed x1        // Declare variable to be imputed
mi impute chained (regress) x1 = y x2, add(10) // Declare imputation model

// Model with imputed data
eststo: mi estimate, post:  regress y x1 x2 // post option is important for -esttab-

// Table
esttab, r2 se mtitles("DGP" "Missing data" "Imputed")