Using programs in Stata

regression-margins.do

// Regression margins visualizer

cap prog drop regmargins
prog def regmargins

  // Syntax command
  syntax anything
    // Expected syntax is like regress
    // Second entry should be factor variable (i.) and we will visualize levels

  // Run regression
  qui reg `anything'

  // Calculate margins
  local categorical : word 2 of `anything'
  local categorical = substr("`categorical'",3,.)

    // Some setup
    qui levelsof `categorical' , local(levels)
    local x = 0
    foreach level in `levels' {
      local ++x
      local plotopts = "`plotopts'" ///
        + " plot`x'opts(lc(none))"
    }

    qui margins `categorical'

  // Graph margins
  marginsplot ///
    , recast(bar) ///
        plotopts(barwidth(0.8)) ///
      by(`categorical')  ///
      plot(`categorical') ///
        `plotopts' ///
      recastci(rspike) ///
        ciopts(lc(black)) ///
      legend(off) xtit(" ") xlab(none) ///
      yscale(r(0)) ylab(#6) ytit(" ")

// End
end

// End of dofile

ritest-demo.do

// Example do-file for programming session

// Define the program
cap prog drop mysample
prog def mysample

// [ritest] requirements
syntax , ///
  [resampvar(string asis)] ///
  [run(string asis)]

qui {

  randtreat ///
  , gen(treatment) replace ///
    strata(strata) unequal(1/3 2/3) ///
    misfits(wglobal)

} // end qui
end

// A basic stratified approach
clear
set obs 10
  gen strata = _n

expand 6
  gen uid = _n

// Use the program to generate "true" sample
mysample

// Generate true outcome DGP with positive treatment effect
gen y = treatment*runiform() + strata + rnormal()

// Use [ritest] to get null distribution
ritest treatment _b[treatment] ///
  , reps(100) samplingprogram(mysample) kdensityplot  ///
  : reg y treatment i.strata

// End of do-file

simulate-demo.do

// Example do-file for programming session

// Define the program
cap prog drop normaldistro
prog def normaldistro , rclass // Allow returning values to memory

syntax
qui {

  clear
  set obs 100
  gen x1 = rnormal() // Arbitrary covariate
  gen rand = rnormal()  // 50-50 treatment
    egen rank = rank(rand)
    gen treatment = rank <= 50

  // DGP
  gen y = x1 + treatment*runiform() // Heterogeneous, positive effect

  reg y treatment
  mat a = r(table)
    return scalar beta = a[1,1]
    return scalar pval = a[4,1]

} // end qui
end

// Use the program
tempfile sims
simulate beta=r(beta) pval=r(pval) ///
  , reps(1000) seed(725485) saving(`sims') ///
  : normaldistro

use `sims' , clear
local style "start(-0.5)  barwidth(0.09) width(.1) fc(gray) freq"
tw ///
  (histogram beta , `style' lc(none) ) ///
  (histogram beta if pval < 0.05 , `style' lc(black) fc(none) ) ///
, xtit("") legend(on ring(0) pos(1) order(2 "p < 0.05") region(lc(none)))

// End of do-file