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.

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

Running Stata from the Command Line

Claude can execute Stata code by running .do files in batch mode from the terminal. This is how to run Stata non-interactively.

Finding the Stata Binary

Stata on macOS is a .app bundle. The actual binary is inside it. Common locations:

# Stata 18 / StataNow (most common)
/Applications/Stata/StataMP.app/Contents/MacOS/stata-mp
/Applications/StataNow/StataMP.app/Contents/MacOS/stata-mp

# Other editions (SE, BE)
/Applications/Stata/StataSE.app/Contents/MacOS/stata-se
/Applications/Stata/StataBE.app/Contents/MacOS/stata-be

If Stata isn't on $PATH, find it with: mdfind -name "stata-mp" | grep MacOS

Batch Mode (`-b`)

# Run a .do file in batch mode — output goes to <filename>.log
/Applications/Stata/StataMP.app/Contents/MacOS/stata-mp -b do analysis.do

# If stata-mp is on PATH (e.g., via symlink or alias):
stata-mp -b do analysis.do

-b = batch mode (non-interactive, no GUI)
Output (everything Stata would display) is written to analysis.log in the working directory
Exit code is 0 on success, non-zero on error
The log file contains all output, including error messages — check it after execution

Running Inline Stata Code

To run a quick Stata snippet without creating a .do file:

# Write a temp .do file and run it
cat > /tmp/stata_run.do << 'EOF'
sysuse auto, clear
summarize price mpg
EOF
stata-mp -b do /tmp/stata_run.do
cat /tmp/stata_run.log

Checking Results

# Check if it succeeded
stata-mp -b do tests/run_tests.do && echo "SUCCESS" || echo "FAILED"

# Search the log for pass/fail
grep -E "PASS|FAIL|error|r\([0-9]+\)" run_tests.log

Tips

clear all at the top of batch scripts — batch mode starts with a fresh Stata session, but clear all ensures no stale state from prior runs in the same session.
set more off — prevents Stata from pausing for --more-- prompts (fatal in batch mode).
Log files overwrite silently — analysis.do always writes to analysis.log in the current directory. If you run multiple .do files, check the right log.
Working directory — Stata's working directory is wherever you run the command from, not where the .do file lives. Use cd in the .do file or absolute paths if needed.

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`	`gmm`, moment conditions, `estat overid`, J-test

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 (`binsreg`, `binstest`)
`packages/nprobust.md`	Nonparametric kernel estimation and inference
`packages/diagnostics.md`	`bacondecomp`, `xttest3`, collinearity, heteroskedasticity tests
`packages/winsor.md`	Winsorizing and trimming: `winsor2`, `winsor`
`packages/data-manipulation.md`	`gtools` (fast collapse/egen), `rangestat`, `egenmore`
`packages/package-management.md`	`ssc install`, `net install`, `ado update`, finding packages

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)

* Event study (uniform timing — must interact with treatment group)
reghdfe y ib(-1).rel_time#1.treated, absorb(id year) vce(cluster id)
testparm *.rel_time#1.treated   // pre-trend test

* 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

stata