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.

Execution model: This skill generates Stata code and saves it as .do files for the user to run manually in Stata. Claude does not execute Stata code directly — there is no MCP server or CLI integration. After generating a .do file, always tell the user exactly which file to open or run and what output to expect.

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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

Never skip tab _merge — it costs nothing and is the only diagnostic you get when assert fails.

merge 1:1 id using other.dta
tab _merge                      // ALWAYS tab before assert
assert _merge == 3              // fails silently without tab output
drop _merge

preserve / restore + tempfile for Collapse-Merge-Back

The standard pattern for computing group stats and merging them onto the original data:

tempfile stats
preserve
collapse (mean) avg_x=x, by(group)
save `stats'
restore
merge m:1 group using `stats'
tab _merge
assert _merge == 3
drop _merge

For simple group means, bysort group: egen avg_x = mean(x) avoids the round-trip entirely.

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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Delivering .do Files to the User

Claude's role is to write correct, well-commented Stata code and save it as a .do file. The user runs it manually in Stata.

Workflow

1. Understand the research task and identify the relevant reference files (see routing table)
2. Write the complete .do file with inline comments explaining each step
3. Save the file to the workspace folder so the user can open it directly
4. Tell the user:
   - The file name and location
   - Any required packages to install first (see packages.md)
   - What the script produces (log, tables, graphs, .dta files, etc.)
   - Any paths or variable names they may need to adjust

.do File Template

Every generated .do file should follow this structure:

/*==============================================================
  Project : [Project name]
  Task    : [What this script does]
  Author  : Generated by Claude
  Date    : [Date]
  Stata   : 16+ recommended
==============================================================*/

* ---------- Preamble ----------
clear all
set more off
version 16

* Set working directory — user should adjust this path
* cd "/path/to/your/data"

* ---------- Required packages ----------
* Install if not already installed:
* ssc install reghdfe, replace
* ssc install estout, replace

* ---------- [Step 1: ...] ----------
// ... code with comments ...

* ---------- [Step 2: ...] ----------
// ...

* ---------- Output ----------
// Tables saved to: results/
// Figures saved to: figures/
// Log: results/analysis.log

log using "results/analysis.log", replace text
// ... estimation code ...
log close

After Delivering the File

Once the .do file is saved, tell the user:

"Open [filename].do in Stata (or run do [filename].do from the Stata command line). The script will produce [describe outputs]. If you encounter any errors, paste the error message here and I'll fix the code."

Do not attempt to execute the .do file yourself or simulate output. Wait for the user to run it and share results if they want further analysis.

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Routing Table

Read only the guide(s) relevant to the user's task. All paths are relative to this SKILL.md file.

Guide	Topics
references/basics.md	Syntax, variables, operators, macros, loops, programming, data import/export, Mata, workflow best practices, external tools integration
references/methods.md	OLS, panel data, DiD, IV/GMM, RDD, treatment effects, matching, time series, MLE, bootstrap, survey methods, sample selection, nonparametric
references/data-mgmt.md	Data management (generate, merge, reshape), string/date/math functions, descriptive statistics, missing data, Mata data access & matrix operations, specialized models (LDV, survival, SEM, spatial, ML)
references/output.md	Graphics (twoway, schemes, coefplot), regression tables (esttab, outreg2, asdoc), summary tables (tabout), export to LaTeX/Word/Excel, graph export best practices
packages.md	Community packages: reghdfe, ivreg2, xtabond2, csdid, rdrobust, synth, psmatch2, binsreg, gtools, winsor2, diagnostics, and package management

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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)

* 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

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Help Improve This Skill

If you produce Stata code with a significant error — wrong syntax, incorrect command usage, or a gotcha you failed to catch — and the issue seems to stem from a gap in these reference files rather than a one-off mistake, consider suggesting to the user that they report it so the documentation can be improved.

When to raise this: Only after you've already corrected the error and the user has working code. Frame it as optional: "I made an error with [X] that I think comes from a gap in the Stata skill documentation. Would you like me to note this for future improvement?"

If the user agrees, help them draft a clear description of the gap and the correct behavior.