ECMT1020 Chap.10 Regression Roadmap and Reproducible Stata
Regression Roadmap and Reproducible Stata
The course moves from probability and inference into classical linear regression, nonlinear specifications, indicator variables, model specification and heteroskedasticity. This chapter organises those topics as an empirical workflow rather than an isolated list of commands.
It shows how to inspect data, document transformations in a Stata do-file, interpret conditional coefficients, diagnose assumptions and preserve an auditable record of the analysis. Simple regression introduces a conditional linear comparison, while multiple regression adds controls and changes what a coefficient holds fixed.
Squared terms, indicators and interactions alter the functional form and must be interpreted together with their reference values. Specification choices affect the economic question, and heteroskedasticity affects the uncertainty calculation.
A reproducible workflow makes these choices visible from raw-data inspection through labelled output, allowing another reader to trace each reported result back to the commands and sample that produced it.
What this chapter covers
- 01
Simple and multiple regression
- 02
Conditional coefficient interpretation
- 03
Nonlinear terms
- 04
Indicator variables and references
- 05
Model specification and heteroskedasticity
- 06
Stata do-files and logs
Worked example · free
Plan a reproducible wage analysis
- +1Load a preserved raw-data version from a known location.
- +1Inspect variable types, units, ranges, missing values and the observational unit.
- +1Create transformations and indicators in documented commands.
- +1Summarise the analysis sample and exclusions.
- +1Estimate the planned model and inspect relevant diagnostics.
- +1Save a log and export labelled evidence linked to the do-file.
Key terms
- Regression coefficient
- A model-based conditional relationship between an outcome and regressor.
- Reference category
- The omitted comparison group for a set of indicator variables.
- Functional form
- The mathematical shape assigned to a relationship in a model.
- Heteroskedasticity
- Changing conditional error variance across observations.
- Do-file
- A saved, ordered set of Stata commands that can be rerun.
- Analysis sample
- The observations actually used by a fitted model after missing values, exclusions and transformations are applied. Its composition should be described explicitly because coefficient estimates and descriptive summaries must refer to the same rows.
- Conditional association
- A model-based relationship between an outcome and regressor while included variables are held fixed. It can be estimated without establishing causality, which additionally requires a design and assumptions addressing omitted determinants and selection.
- Reproducible workflow
- An ordered analysis that can be rerun from preserved inputs to labelled outputs. It records transformations, sample decisions, models and diagnostics rather than relying on interactive history.
Regression Roadmap and Reproducible Stata FAQ
Why use a do-file?
It preserves commands in order, makes transformations and model changes visible, and allows the analysis to be rerun from a clean start. Command history alone is fragile and incomplete.
How do indicator coefficients work?
An indicator coefficient compares its category with an omitted reference group, conditional on other included variables. The reference must be named in the interpretation of magnitude and direction.
What changes with a squared term?
The marginal relationship depends on the current regressor value and combines the linear and squared coefficients. The linear coefficient alone is not a constant effect across all observations in that specification and sample.
Exam move
Write the economic question and intended coefficient interpretation before opening software. In Stata, use one do-file that loads preserved raw data, inspects structure, documents transformations, defines the analysis sample, estimates the model and saves labelled output. Rehearse interpretations with units, reference groups and held-fixed variables, then add one sentence separating conditional association from causal claims.
Organise the do-file into auditable stages. Begin with a clear location and preserved raw dataset, then inspect variable names, types, labels, ranges and missingness. Confirm what one row represents before generating anything. Place cleaning and transformation commands in a dedicated section with comments explaining the reason for each decision.
A comment should record why a category is omitted or why a logarithm is used, because the command already records what happened. Produce descriptive summaries for the exact analysis sample, not a broader dataset that later loses observations. State the baseline specification in words before running it. For each coefficient, note outcome units, regressor units, reference category and included controls.
If the model contains a square or interaction, write the combined marginal relationship and evaluate it at a stated value rather than reading one coefficient alone. Keep diagnostics after the baseline result so revisions can be traced. When heteroskedasticity is relevant, separate the coefficient estimate from the reliability of its standard error.
Do not let a change in reported uncertainty be described as a change in the fitted coefficient unless it actually changes. Save a log and export labelled tables or graphs from code. Rerun the complete file in a clean session to detect hidden interactive steps, then compare the recreated output with the intended submission.
For exam practice, translate a small results table without software: identify the conditional comparison, interpret magnitude and uncertainty, and state why causality requires additional assumptions. Maintain an error log for wrong reference groups, unit mistakes, isolated interpretation of nonlinear terms, undocumented sample exclusions and causal overstatement.
The closing sentence should return to the original economic question and make a claim no stronger than the data and design support.
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