Econometric Analysis Using Stata Training Course

Participants can create analysis-ready Stata datasets from common source files.

• Importing Excel, CSV and delimited files with explicit variable definitions
• Variable labels, value labels and formats documented for later review
• Missing values identified, coded consistently and assessed for analytical consequences
• Duplicate records detected using identifier checks and duplicate-report commands
• Merging master and using files with one-to-one and many-to-one keys
• Reshaping wide and long datasets for repeated-observation analysis

Participants can assess data quality and describe distributions before modelling.

• Codebooks produced to inspect types, ranges, labels and missingness
• Summary statistics matched to continuous, categorical and skewed variables
• Histograms, box plots and scatterplots reveal distributions and unusual observations
• Cross-tabulations examine categorical relationships and sparse-cell problems
• Outliers investigated using domain evidence rather than automatic deletion
• Transformations considered for scales, skewness, comparability and model assumptions

Participants can interpret sampling uncertainty and conduct standard statistical tests.

• Sampling variation linked to standard errors, confidence intervals and precision
• Null and alternative hypotheses specified for decision-relevant parameter tests
• t tests compare estimated coefficients against stated benchmark values
• F tests assess joint restrictions across multiple regression parameters
• Statistical significance separated from economic and operational materiality
• Confidence intervals reported alongside point estimates and practical interpretation

Participants can estimate and interpret multiple linear regression models in Stata.

• Ordinary least squares estimates conditional mean relationships between variables
• Regression equations specify dependent variables, predictors and intercept terms
• Coefficients interpreted in units, percentages and marginal changes
• Adjusted R-squared assessed without confusing fit with causal validity
• Factor-variable notation models categorical predictors and interaction effects
• Margins and marginal-effects plots communicate conditional model predictions

Participants can choose and compare functional forms appropriate to the data-generating process.

• Linear, log-linear and log-log specifications matched to analytical questions
• Elasticities calculated from logarithmic models and interpreted carefully
• Polynomial terms represent curved relationships within observed data ranges
• Interactions test whether estimated relationships differ between defined groups
• Ramsey RESET tests flag possible functional-form and omitted-variable problems
• Information criteria compare non-nested models without replacing substantive judgement

Participants can diagnose common regression problems and assess influential observations.

• Residual-versus-fitted plots assess linearity and variance patterns
• Normal probability plots examine residual distributional departures
• Variance inflation factors identify potentially harmful multicollinearity
• Cook's distance highlights observations with disproportionate coefficient influence
• Leverage and studentised residuals distinguish unusual predictor and outcome values
• Diagnostic findings documented with corrective actions and unresolved limitations

Participants can select standard-error treatments for non-ideal error structures.

• Heteroskedasticity identified using residual patterns and Breusch-Pagan testing
• Huber-White robust standard errors correct heteroskedastic inference procedures
• Clustered standard errors address within-group correlated disturbances
• Serial correlation diagnosed for ordered observations and repeated time periods
• Newey-West standard errors address autocorrelation and heteroskedasticity jointly
• Coefficient estimates distinguished from their estimated sampling uncertainty

Participants can build and document a complete regression workflow from source data.

• Analysis samples defined through transparent inclusion and exclusion rules
• Derived variables generated with named formulas and validation checks
• Baseline models established before adding controls and alternative specifications
• Nested specifications compared to assess coefficient stability and sensitivity
• Estimation results stored for organised comparison across candidate models
• Commented do-files reproduce every dataset, model, table and figure

Participants can report regression evidence accurately for technical and non-technical readers.

• Regression tables report estimates, uncertainty, samples and model specifications
• Coefficient narratives state direction, magnitude, confidence and relevant units
• Predicted values translated into decision-relevant scenarios and comparisons
• Assumptions and limitations presented beside substantive model findings
• Model comparisons reported without selective omission of inconvenient estimates
• Graphs designed with labelled axes, uncertainty intervals and source notes

Participants can prepare and inspect panel datasets with entities observed over time.

• Panel identifiers define entities, time indices and observation frequency
• xtset declares panel structure and identifies gaps or duplicates
• Balanced and unbalanced panels compared for coverage and attrition
• Within-entity and between-entity variation examined before estimation
• Lagged and lead variables created with declared time ordering
• Panel descriptive tables reveal trends, turnover and missing observations

Participants can estimate and compare standard panel-data models.

• Fixed-effects models control for time-invariant entity-specific characteristics
• Random-effects models require independence between effects and explanatory variables
• Hausman tests inform fixed-effects and random-effects model selection
• First-difference estimators model changes between consecutive observations
• Time fixed effects control common shocks and calendar patterns
• Clustered panel standard errors support valid within-entity inference

Participants can estimate and communicate models for binary outcomes.

• Linear probability, logit and probit models compared for binary outcomes
• Maximum likelihood estimation interpreted without treating coefficients as probabilities
• Odds ratios reported carefully for logit model results
• Average marginal effects express predictor changes in probability units
• Classification tables assess predictions using stated decision thresholds
• Receiver operating characteristic curves compare binary model discrimination

Participants can identify endogeneity risks and estimate basic instrumental-variables models.

• Omitted variables, simultaneity and measurement error create endogeneity bias
• Instrument relevance requires a strong empirical relationship with treatment
• Exclusion restrictions require instruments affect outcomes only through treatment
• Two-stage least squares estimates causal parameters using valid instruments
• First-stage diagnostics assess weak-instrument risks before interpretation
• Instrumental-variable results reported with assumptions, tests and local interpretation

Participants can estimate difference-in-differences models and assess their identification assumptions.

• Treatment, comparison group, pre-period and post-period defined explicitly
• Difference-in-differences estimates compare changes between treatment and comparison groups
• Parallel-trends assumptions assessed using pre-treatment outcome trajectories
• Event-study coefficients display dynamic effects around intervention timing
• Staggered treatment timing requires careful estimator and comparison choices
• Policy findings distinguish estimated effects from untested implementation claims

Participants can prepare time-series data and model trend and seasonal components.

• Time-series declaration establishes frequency, dates and ordered observations
• Trend, seasonality, cycles and irregular movements distinguished graphically
• Stationarity assessed through plots, autocorrelation functions and unit-root tests
• First differences and logarithms stabilise selected time-series processes
• Autocorrelation and partial autocorrelation guide tentative model identification
• Seasonal indicators capture recurring calendar effects in regression models

Participants can estimate ARIMA forecasts and evaluate forecast performance.

• Autoregressive and moving-average terms model serial dependence patterns
• ARIMA orders selected using diagnostics, information criteria and forecasting purpose
• Residual autocorrelation checked using correlograms and Ljung-Box tests
• Dynamic forecasts generated with stated estimation and forecast windows
• Forecast accuracy measured using MAE, RMSE and mean absolute percentage error
• Benchmark forecasts compared against ARIMA estimates before operational use

Participants can assess whether findings remain credible under reasonable analytical alternatives.

• Train-test splits separate model development from predictive performance assessment
• Cross-validation estimates out-of-sample error for prediction-focused models
• Alternative variable definitions test sensitivity to measurement choices
• Placebo tests examine whether causal designs generate implausible effects
• Subgroup analyses distinguish planned heterogeneity from data-mining claims
• Model selection records document rejected specifications and selection rationale

Participants can apply governance controls to the development and review of econometric analyses.

• Data provenance records source, extraction date, ownership and permitted use
• Personally identifiable information minimised before analytical dataset creation
• Variable definitions reviewed for proxy bias and measurement inequity
• Peer review checks code, assumptions, outputs and interpretation claims
• Model documentation states intended use, exclusions and known limitations
• Decision-makers receive uncertainty statements proportionate to analytical evidence

Participants can build, review and present a complete reproducible econometric analysis addressing a defined decision question.

• Decision question converted into estimands, hypotheses and measurable outcome variables
• Source data cleaned, merged and documented through commented Stata code
• Econometric method selected from data structure and identification requirements
• Diagnostic evidence used to revise specifications and inference procedures
• Results tables and figures prepared for technical and executive audiences
• Findings presented with assumptions, limitations, recommendations and supporting code