stata

name	stata
description	Comprehensive Stata reference for writing correct .do files, data management, econometrics, causal inference, graphics, Mata programming, and 17+ community packages (reghdfe, estout, did, rdrobust, etc.). Covers syntax, options, gotchas, and idiomatic patterns. Use this skill whenever the user asks you to write, debug, or explain Stata code.
triggers	["stata",".do file","do-file","regress","regression in stata","panel data","fixed effects","reghdfe","estout","esttab","outreg2","difference-in-differences","event study","propensity score","rdrobust","synthetic control","xtset","merge","reshape","collapse","egen","ssc install","mata","putexcel","putdocx","graph export","survival analysis","heckman","tobit","logit","probit","arima","[Truncated]"]

Stata Skill

You have access to comprehensive Stata reference files. Do not load all files. Read only the 1-3 files relevant to the user's current task using the routing table below.

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Critical Gotchas

These are Stata-specific pitfalls that lead to silent bugs. Internalize these before writing any code.

Missing Values Sort to +Infinity

Stata's . (and .a-.z) are greater than all numbers.

* WRONG — includes observations where income is missing!
gen high_income = (income > 50000)

* RIGHT
gen high_income = (income > 50000) if !missing(income)

* WRONG — missing ages appear in this list
list if age > 60

* RIGHT
list if age > 60 & !missing(age)

= vs ==

= is assignment; == is comparison. Mixing them up is a syntax error or silent bug.

* WRONG — syntax error
gen employed = 1 if status = 1

* RIGHT
gen employed = 1 if status == 1

Local Macro Syntax

Locals use `name' (backtick + single-quote). Globals use $name or ${name}. Forgetting the closing quote is the #1 macro bug.

local controls "age education income"
regress wage `controls'        // correct
regress wage `controls         // WRONG — missing closing quote
regress wage 'controls'        // WRONG — wrong quote characters

by Requires Prior Sort (Use bysort)

* WRONG — error if data not sorted by id
by id: gen first = (_n == 1)

* RIGHT — bysort sorts automatically
bysort id: gen first = (_n == 1)

* Also RIGHT — explicit sort
sort id
by id: gen first = (_n == 1)

Factor Variable Notation (i. and c.)

Use i. for categorical, c. for continuous. Omitting i. treats categories as continuous.

* WRONG — treats race as continuous (e.g., race=3 has 3x effect of race=1)
regress wage race education

* RIGHT — creates dummies automatically
regress wage i.race education

* Interactions
regress wage i.race##c.education    // full interaction
regress wage i.race#c.education     // interaction only (no main effects)

generate vs replace

generate creates new variables; replace modifies existing ones. Using generate on an existing variable name is an error.

gen x = 1
gen x = 2          // ERROR: x already defined
replace x = 2      // correct

String Comparison Is Case-Sensitive

* May miss "Male", "MALE", etc.
keep if gender == "male"

* Safer
keep if lower(gender) == "male"

merge Always Check _merge

merge 1:1 id using other.dta
tab _merge                      // always inspect
assert _merge == 3              // or handle mismatches
drop _merge

preserve / restore for Temporary Changes

preserve
collapse (mean) income, by(state)
* ... do something with collapsed data ...
restore   // original data is back

Weights Are Not Interchangeable

• fweight — frequency weights (replication)
• aweight — analytic/regression weights (inverse variance)
• pweight — probability/sampling weights (survey data, implies robust SE)
• iweight — importance weights (rarely used)

capture Swallows Errors

capture some_command
if _rc != 0 {
    di as error "Failed with code: " _rc
    exit _rc
}

Line Continuation Uses ///

regress y x1 x2 x3 ///
    x4 x5 x6, ///
    vce(robust)

Stored Results: r() vs e() vs s()

• r() — r-class commands (summarize, tabulate, etc.)
• e() — e-class commands (estimation: regress, logit, etc.)
• s() — s-class commands (parsing)

A new estimation command overwrites previous e() results. Store them first:

regress y x1 x2
estimates store model1

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Routing Table

Read only the files relevant to the user's task. Paths are relative to this SKILL.md file.

Data Operations

File	Topics & Key Commands
references/basics-getting-started.md	use, save, describe, browse, sysuse, basic workflow
references/data-import-export.md	import delimited, import excel, ODBC, export, web data
references/data-management.md	generate, replace, merge, append, reshape, collapse, recode, egen, encode/decode
references/variables-operators.md	Variable types, byte/int/long/float/double, operators, missing values (.<.a), if/in qualifiers
references/string-functions.md	substr(), regexm(), strtrim(), split, ustrlen(), regex, Unicode
references/date-time-functions.md	date(), clock(), %td/%tc formats, mdy(), dofm(), business calendars
references/mathematical-functions.md	round(), log(), exp(), abs(), mod(), cond(), distributions, random numbers

Statistics & Econometrics

File	Topics & Key Commands
references/descriptive-statistics.md	summarize, tabulate, correlate, tabstat, codebook, weighted stats
references/linear-regression.md	regress, vce(robust), vce(cluster), test, lincom, margins, predict, ivregress
references/panel-data.md	xtset, xtreg fe/re, Hausman test, xtabond, dynamic panels
references/time-series.md	tsset, ARIMA, VAR, dfuller, pperron, irf, forecasting
references/limited-dependent-variables.md	logit, probit, tobit, poisson, nbreg, mlogit, ologit, margins for nonlinear
references/bootstrap-simulation.md	bootstrap, simulate, permute, Monte Carlo
references/survey-data-analysis.md	svyset, svy:, subpop(), complex survey design, replicate weights
references/missing-data-handling.md	mi impute, mi estimate, FIML, misstable, diagnostics
references/maximum-likelihood.md	ml model, custom likelihood functions, ml init, gradient-based optimization
references/gmm-estimation.md

Causal Inference

File	Topics & Key Commands
references/treatment-effects.md	teffects ra/ipw/ipwra/aipw, stteffects, ATE/ATT/ATET
references/difference-in-differences.md	DiD, parallel trends, event studies, staggered adoption
references/regression-discontinuity.md	Sharp/fuzzy RD, bandwidth selection, rdplot
references/matching-methods.md	PSM, nearest neighbor, kernel matching, teffects nnmatch
references/sample-selection.md	heckman, heckprobit, treatment models, exclusion restrictions

Advanced Methods

File	Topics & Key Commands
references/survival-analysis.md	stset, stcox, streg, Kaplan-Meier, parametric models
references/sem-factor-analysis.md	sem, gsem, CFA, path analysis, alpha, reliability
references/nonparametric-methods.md	kdensity, rank tests, qreg, npregress
references/spatial-analysis.md	spmatrix, spregress, spatial weights, Moran's I
references/machine-learning.md	lasso, elasticnet, cvlasso, cross-validation

Graphics

File	Topics & Key Commands
references/graphics.md	twoway, scatter, line, bar, histogram, graph combine, graph export, schemes

Programming

File	Topics & Key Commands
references/programming-basics.md	local, global, foreach, forvalues, program define, syntax, return
references/advanced-programming.md	syntax, mata, classes, _prefix, dialog boxes, tempfile/tempvar
references/mata-introduction.md	Mata basics, when to use Mata vs ado, data types
references/mata-programming.md	Mata functions, flow control, structures, pointers
references/mata-matrix-operations.md	Matrix creation, decompositions, solvers, st_matrix()
references/mata-data-access.md	st_data(), st_view(), st_store(), performance tips

Output & Workflow

File	Topics & Key Commands
references/tables-reporting.md	putexcel, putdocx, putpdf, LaTeX integration, collect
references/workflow-best-practices.md	Project structure, master do-files, version control, debugging, common mistakes
references/external-tools-integration.md	Python via python:, R via rsource, shell commands, Git

Community Packages

File	What It Does
packages/reghdfe.md	High-dimensional fixed effects OLS (absorbs multiple FE sets efficiently)
packages/estout.md	esttab/estout: publication-quality regression tables
packages/outreg2.md	Alternative regression table exporter (Word, Excel, TeX)
packages/asdoc.md	One-command Word document creation for any Stata output
packages/tabout.md	Cross-tabulations and summary tables to file
packages/coefplot.md	Coefficient plots from stored estimates
packages/graph-schemes.md	grstyle, schemepack, plotplain — better graph themes
packages/did.md	Modern DiD: csdid, did_multiplegt, did_imputation (Callaway-Sant'Anna, de Chaisemartin-D'Haultfoeuille, Borusyak-Jaravel-Spiess)
packages/event-study.md	eventstudyinteract, eventdd — event study estimators
packages/rdrobust.md	Robust RD estimation with optimal bandwidth (rdrobust, rdplot, rdbwselect)
packages/psmatch2.md	Propensity score matching (nearest neighbor, kernel, radius)
packages/synth.md	Synthetic control method (synth, synth_runner)
packages/ivreg2.md	Enhanced IV/2SLS: ivreg2, xtivreg2 with additional diagnostics
packages/xtabond2.md	Dynamic panel GMM (Arellano-Bond/Blundell-Bond)
packages/binsreg.md	Binned scatter plots with CI (, )

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Common Patterns

Regression Table Workflow

* Estimate models
eststo clear
eststo: regress y x1 x2, vce(robust)
eststo: regress y x1 x2 x3, vce(robust)
eststo: regress y x1 x2 x3 x4, vce(cluster id)

* Export table
esttab using "results.tex", replace ///
    se star(* 0.10 ** 0.05 *** 0.01) ///
    label booktabs ///
    title("Main Results") ///
    mtitles("(1)" "(2)" "(3)")

Panel Data Setup

xtset panelid timevar          // declare panel structure
xtdescribe                      // check balance
xtsum outcome                   // within/between variation

* Fixed effects
xtreg y x1 x2, fe vce(cluster panelid)
* Or with reghdfe (preferred for multiple FE)
reghdfe y x1 x2, absorb(panelid timevar) vce(cluster panelid)

Difference-in-Differences

* Classic 2x2 DiD
gen post = (year >= treatment_year)
gen treat_post = treated * post
regress y treated post treat_post, vce(cluster id)

* Modern staggered DiD (Callaway & Sant'Anna)
csdid y x1 x2, ivar(id) time(year) gvar(first_treat) agg(event)
csdid_plot

Graph Export

* Publication-quality scatter with fit line
twoway (scatter y x, mcolor(navy%50) msize(small)) ///
       (lfit y x, lcolor(cranberry) lwidth(medthick)), ///
    title("Title Here") ///
    xtitle("X Label") ytitle("Y Label") ///
    legend(off) scheme(s2color)
graph export "figure1.pdf", replace as(pdf)
graph export "figure1.png", replace as(png) width(2400)

Data Cleaning Pipeline

* Load and inspect
import delimited "raw_data.csv", clear varnames(1)
describe
codebook, compact

* Clean
rename *, lower                 // lowercase all varnames
destring income, replace force  // convert string to numeric
replace income = . if income < 0

* Label
label variable income "Annual household income (USD)"
label define yesno 0 "No" 1 "Yes"
label values employed yesno

* Save
compress
save "clean_data.dta", replace

Multiple Imputation

mi set mlong
mi register imputed income education
mi impute chained (regress) income (ologit) education = age i.gender, add(20) rseed(12345)
mi estimate: regress wage income education age i.gender