Regression
2026-05-3017 min read

poisson in Stata: Count Models, Exposure, and Robust SE

Run poisson stata in Stata with coefficient interpretation, inference checks, and practical modeling decisions for real datasets.

Sytra Team
Research Engineering Team, Sytra AI

You are applying poisson stata under deadline pressure, and one unnoticed data issue can invalidate the full analysis pass.

You will execute estimation with interpretation and diagnostics that hold up in review. This guide keeps the path anchored to estimating wage models with defensible standard errors and postestimation output.

All examples tested in Stata 18 SE. Compatible with Stata 15+.

Quick Answer

  1. Start with a defined research task before running poisson stata.
  2. Run poisson only after preflight checks on keys, types, and missingness.
  3. Audit command output immediately and document expected vs observed counts.
  4. Add a reusable QA block focused on specification checks, inference assumptions, and output interpretation.

Execution Blueprint: poisson stata for estimating wage models with defensible standard errors and postestimation output

Anchor the use case and run preflight checks

This workflow is built for estimating wage models with defensible standard errors and postestimation output. Model syntax may run, but inference can still fail review when diagnostics are missing.

Run a deterministic setup first so every command in later sections executes against known data structure and known variable types.

If you are extending this pipeline, also review Panel Diagnostics in Stata: xtdescribe, xtsum, and Balance Checks and destring and real() in Stata: Convert String Numbers Safely.

poisson-stata-count-model-setup.do
stata
1clear all
2version 18
3set seed 260210
4set obs 1200
5gen firm_id = ceil(_n/12)
6gen year = 2014 + mod(_n,10)
7gen worker_id = _n
8gen education = 10 + floor(runiform()*8)
9gen wage = 18 + 0.8*education + 0.2*(year-2014) + rnormal(0,2)
10
11* Preflight checks
12assert !missing(firm_id, year)
13assert !missing(wage, education)
14count
. count
  1200
๐Ÿ’กUse realistic variable names
Keep names like wage, education, firm_id, and year so collaborators can audit logic quickly.

Execute poisson with full diagnostics

Run poisson as its own block and inspect output before proceeding. This preserves a clean debug boundary and supports peer review.

The command example below is complete and runnable; it is designed to mirror real panel workflows rather than toy x/y placeholders.

poisson-stata-count-model-execution.do
stata
1clear all
2version 18
3set seed 260210
4set obs 1200
5gen firm_id = ceil(_n/12)
6gen year = 2014 + mod(_n,10)
7gen worker_id = _n
8gen education = 10 + floor(runiform()*8)
9gen wage = 18 + 0.8*education + 0.2*(year-2014) + rnormal(0,2)
10
11* Preflight checks
12assert !missing(firm_id, year)
13assert !missing(wage, education)
14count
15
16* ---- Section-specific continuation ----
17* Core execution block for poisson stata
18gen patents = round(exp(1 + 0.05*education + rnormal(0,0.2)))
19poisson patents c.education i.year, vce(robust)
20
21* Immediate output audit
22count
. count
  1200
โš ๏ธAudit before moving to the next stage
Immediately inspect outputs after each command block to prevent silent pipeline drift.

Harden for production: assertions, logs, and reusable checks

After command execution, enforce specification checks, inference assumptions, and output interpretation so downstream inference and exports remain stable across reruns.

This final block makes the workflow team-ready: logs are captured, failures are explicit, and diagnostics are repeatable.

poisson-stata-count-model-qa.do
stata
1clear all
2version 18
3set seed 260210
4set obs 1200
5gen firm_id = ceil(_n/12)
6gen year = 2014 + mod(_n,10)
7gen worker_id = _n
8gen education = 10 + floor(runiform()*8)
9gen wage = 18 + 0.8*education + 0.2*(year-2014) + rnormal(0,2)
10
11* Preflight checks
12assert !missing(firm_id, year)
13assert !missing(wage, education)
14count
15
16* ---- Section-specific continuation ----
17* Production hardening block
18capture log close
19log using poisson-stata-count-model-qa.log, text replace
20
21gen patents = round(exp(1 + 0.05*education + rnormal(0,0.2)))
22poisson patents c.education i.year, vce(robust)
23
24quietly regress wage c.education i.year, vce(robust)
25estat ic
26predict ehat, resid
27summ ehat
28log close
. estat ic
. estat ic

Akaike's information criterion and Bayesian information criterion
๐Ÿ’กKeep a reusable QA footer
A standard QA footer with assert and count checks prevents repeat debugging in future projects.

Common Errors and Fixes

"factor variables may not contain noninteger values"

A factor variable was not integer encoded.

Encode categories or switch to continuous notation for truly numeric variables.

. regress wage i.education
factor variables may not contain noninteger values
r(452);
This causes the error
wrong-way.do
stata
regress wage i.education
This is the fix
right-way.do
stata
regress wage c.education
error-fix.do
stata
1summ education
2regress wage c.education i.year
. regress wage c.education i.year
Linear regression

Number of obs   =      1,200
F(10, 1189)     =      42.61

Command Reference

poisson

Stata docs โ†’

Primary command reference for poisson stata workflows in Stata.

poisson depvar indepvars [, vce(robust)]
Preflight checksValidate keys, types, and missingness before execution
Execution blockRun the command in an isolated, reviewable section
DiagnosticsInspect output immediately and compare against expectations
QA footerKeep assertions and logs for reproducible reruns

How Sytra Handles This

Sytra can execute poisson stata as a staged workflow: preflight validation, runnable Stata code generation, and QA assertions before final output.

A direct natural-language prompt for this exact workflow:

sytra-prompt.txt
bash
Execute poisson stata for a firm_id-year wage dataset. Use variables wage, education, firm_id, and year. Include preflight checks, runnable Stata code, output diagnostics, and post-command assertions with a log file.

Sytra catches these errors before you run.

Sytra can execute poisson stata as a staged workflow: preflight validation, runnable Stata code generation, and QA assertions before final output.

Join the Waitlist โ†’

FAQ

What is the safest order for poisson stata in a production do-file?

Use a three-step order: preflight checks, poisson execution, and post-command assertions. This sequence catches breakpoints before models or exports depend on the result.

How do I verify that poisson stata did not damage my sample?

Track count before and after each transformation, then validate key uniqueness and missingness changes on core variables. Keep those checks in the script, not in ad hoc console runs.

Which Stata versions are compatible with this workflow?

All examples are tested in Stata 18 SE and are compatible with Stata 15+, with installation checks included when community packages are used.

Related Guides

Written by Sytra Team
Research Engineering Team, Sytra AI

We build practical, reproducible workflows for Stata and R teams working on real empirical research pipelines.

#Stata#poisson#Regression

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