Stata Help for Econometrics, Panel Data and Survey Analysis

What our Stata help covers ¶

Start every project with a do-file and a log file. If it is not in the do-file, it did not happen.

Stata help at Copperplate is one-to-one support for researchers and students using Stata on their own data. A consultant works with you on data management, model choice, estimation, diagnostics and reporting, and explains the output so you can interpret and defend it. You can also book sessions as a Stata tutor to learn the commands step by step.

Stata's built-in help command is excellent for syntax: help regress or help egen opens the manual entry. What it cannot tell you is whether a command suits your research design. That is where we come in. For questions about test choice and study design that apply to any software, see our statistics help and consulting page.

Data management in Stata: generate, replace, egen, reshape and merge ¶

Most of the time in an applied project goes into preparing data. We help you write do-files that do this transparently and reproducibly.

• generate and replace: create variables and recode values, for example replace income = . if income == -99.
• egen: group summaries and row functions, such as egen mean_wage = mean(wage), by(industry) or egen index = rowmean(q1-q5).
• bysort: operations within groups, such as bysort firm_id (year): generate lag_sales = sales[_n-1].
• reshape: switch between wide and long formats, for example reshape long income, i(person_id) j(year).
• merge: combine files with merge 1:1 firm_id year using prices or merge m:1, then check tab _merge before dropping unmatched cases.
• Labels and types: label define, encode, destring and recode.

Econometrics and panel data in Stata ¶

Regression and limited dependent variables

We support regress with vce(robust) or vce(cluster id), factor-variable notation such as i.region and c.age##c.age, instrumental variables with ivregress 2sls, and binary outcomes with logit and probit. After estimation we use margins and margins, dydx(*) to turn coefficients into average marginal effects that readers can understand, and test, estat vif and predict for diagnostics.

Panel data models

For panel data we declare the structure with xtset firm_id year, then compare pooled, fixed-effects and random-effects models with xtreg, fe and xtreg, re. The Hausman test needs stored estimates (estimates store fe, estimates store re, then hausman fe re), and we explain its limits when errors are clustered. We also help with year fixed effects via i.year, difference-in-differences designs, and dynamic panels with xtabond or xtdpdsys.

Survey data analysis in Stata ¶

Using if instead of subpop() with svy data is one of the most common errors we correct in Stata projects.

Survey datasets such as the DHS, national labor force surveys or health interview surveys use complex sampling. Ignoring it gives standard errors that are too small. We help you read the survey documentation and set up the design correctly:

• Declare the design with svyset psu [pweight = wt], strata(stratum).
• Estimate with the svy: prefix, for example svy: mean, svy: tabulate, svy: regress and svy: logit.
• Analyze subgroups with svy, subpop(female): rather than if, which would drop observations the variance estimator needs.

Stata tables, graphs and reporting results ¶

We help you move from output to publication-ready results. Descriptive tables come from tabulate, summarize and the redesigned table command in Stata 17 and later, such as table region sex, statistic(mean income). Confidence intervals come from ci means wage or from the estimation output itself. Graphs include graph box wage, over(industry) for box plots and twoway for scatter and line plots.

For regression tables we use esttab from the user-written estout package (ssc install estout), or etable in Stata 17 and later, exporting to Word or LaTeX with standard errors and model statistics.

We format tables to your journal's style or to APA table format, and review your regression equation notation.

How Stata help works ¶

1. Send your files: anonymized .dta data, existing do-files and logs, your research questions and any reviewer or supervisor comments.
2. Get a scope and quote: we confirm what needs to be done and how. See pricing.
3. Work together: screen-shared sessions or a commented do-file that you run and check yourself.
4. Review your write-up: results and tables come back with Track Changes and margin comments.
5. Ask follow-up questions about any comment after delivery.

Turnaround runs from 24 hours to 7 days depending on scope. Files are handled confidentially, an NDA is available on request, and we delete files when you ask. Sessions are online, so if you were looking for a Stata tutor near you, location is no barrier.

Stata help and academic integrity ¶

We support, explain and check. We do not complete graded assignments, problem sets or exams, and we do not produce work for you to submit as your own. In a consulting project you run the do-file, you decide on the final specification and you write the interpretation. Your consultant explains the options and their trade-offs, and flags problems such as omitted fixed effects or misdeclared survey designs.

This matters in practice. Examiners and referees often ask why you chose a particular estimator or clustering level, and you need to answer from your own understanding. Rules on outside statistical help also differ between universities and departments, so please check your institution's policy. Our academic integrity policy explains our approach.