Advanced Stata Programming Training Course

Participants can import, standardise and validate multi-source analytical datasets.

• Importing Excel, CSV, fixed-width, JSON, and delimited financial data
• Unicode strings, date conversions, encodings, and locale-specific numeric formats
• Data dictionaries generated from describe, codebook, and metadata inspection
• Validation rules using assert, confirm, duplicates, and missingness checks
• Financial identifiers standardised across entities, securities, reporting periods, and currencies
• Automated import routines using file lists and parameterised do-files

Participants can manage linked datasets and complex transformations without unnecessary disk operations.

• Frames for separate datasets, linked records, and temporary calculation spaces
• frlink and frget applied to controlled cross-frame data retrieval
• Merge diagnostics using _merge, uniqueness checks, and reconciliation counts
• Reshape, collapse, contract, and statsby for analytical data transformations
• By-group calculations with egen, rangestat, and rolling-window statistics
• Data signatures and datasignature used to detect unintended dataset changes

Participants can automate repetitive Stata tasks through controlled program flow and macro design.

• Local and global macros scoped to prevent unintended value substitution
• foreach and forvalues loops generating variables, models, and output series
• while loops controlled through counters, conditions, and termination tests
• Macro extended functions parsing variable lists, filenames, and labels
• Conditional execution with if, else, continue, break, and capture
• Stored results harvested from e(), r(), s(), and c()

Participants can create parameterised Stata programs with documented inputs, outputs and error controls.

• program define syntax supporting varlists, options, weights, and qualifiers
• syntax command parsing required variables, optional parameters, and defaults
• marksample and markout controlling observations included in program execution
• return, ereturn, and sreturn used for structured program outputs
• Error handling through confirm, capture, exit codes, and custom messages
• Ado-file conventions, help files, and program documentation standards

Participants can use Mata to implement efficient matrix calculations and custom estimators.

• Mata objects, views, scalars, vectors, matrices, and string matrices
• st_data and st_store transferring data between Stata and Mata
• Matrix algebra for least squares, covariance matrices, and transformations
• Vectorised Mata calculations replacing slow observation-by-observation Stata loops
• Functions, pointers, structs, and libraries for reusable Mata code
• Custom numerical routines checked against built-in Stata estimation results

Participants can build panel-data pipelines and assess appropriate fixed-effects or random-effects specifications.

• xtset declarations, panel gaps, duplicates, and time-index integrity checks
• Fixed-effects, random-effects, and correlated random-effects model specifications
• Within transformations and demeaning implemented through programmed routines
• Clustered standard errors selected for entity, time, and two-way dependence
• Hausman testing, Mundlak terms, and specification comparison workflows
• Dynamic panel models using xtabond2 assumptions and diagnostic statistics

Participants can prepare financial time series and programme common market-data analyses.

• tsset declarations, trading calendars, gaps, and irregular observation dates
• Log returns, excess returns, cumulative returns, and volatility measures
• Rolling regressions using rolling, rangestat, and programmed estimation loops
• ARIMA, ARCH, and GARCH specifications for conditional volatility modelling
• Unit-root tests using dfuller, pperron, and stationarity decision rules
• Event-study windows, abnormal returns, and cumulative abnormal-return calculations

Participants can programme transparent causal estimation workflows with diagnostic and sensitivity checks.

• Potential-outcomes notation linked to treatment, outcome, covariates, and estimands
• Propensity-score models with overlap diagnostics and balance assessment
• Inverse-probability weighting using teffects and stabilised weight calculations
• Difference-in-differences models with parallel-trends evidence and event-time indicators
• Instrumental-variable estimation using ivregress, relevance tests, and exclusion reasoning
• Regression discontinuity designs with bandwidth choices and manipulation tests

Participants can quantify estimator uncertainty through programmed resampling and simulation procedures.

• Bootstrap procedures using bsample, bootstrap prefixes, and custom returned statistics
• Permutation tests implemented with random reassignment and reference distributions
• Monte Carlo simulations assessing bias, coverage, power, and convergence
• simulate command collecting program outputs across repeated data-generating processes
• Random-number streams, seed management, and reproducible simulation designs
• Power analysis using power commands and programmed scenario comparisons

Participants can build diagnostic checks that identify data, specification and inference weaknesses.

• Residual diagnostics for heteroskedasticity, serial correlation, normality, and influential observations
• Multicollinearity assessment using variance inflation factors and condition indices
• Specification tests including linktest, ovtest, and functional-form comparisons
• Outlier treatment documented through winsorisation, trimming, and influence analysis
• Automated assertion checks for sample sizes, coefficients, and missing outputs
• Sensitivity analysis across specifications, samples, transformations, and standard-error choices

Participants can profile and improve code performance for large or repeated analytical workloads.

• Timer profiling identifies slow commands, loops, merges, and repeated calculations
• Preserve and restore replaced by frames and targeted temporary objects
• Efficient joins using merge, frlink, joinby, and range-based matching
• Variable storage types selected to reduce memory use and precision loss
• Collapse-first strategies reduce observation counts before expensive model estimation
• Parallel processing considerations, reproducibility constraints, and independent task design

Participants can produce consistent statistical outputs directly from programmed Stata analyses.

• collect framework structures regression results, descriptive statistics, and custom labels
• etable and esttab produce formatted model comparison tables
• putexcel writes audit-ready tables, formulas, and formatting to workbooks
• putdocx generates Word reports with text, tables, and figures
• Graph schemes, by-options, annotations, and export settings standardised programmatically
• Results datasets created with postfile for downstream reporting and review

Participants can acquire and document external data inputs for repeatable economic analysis.

• import fred retrieves macroeconomic series with frequency and transformation controls
• World Bank API queries filtered by indicator, economy, and period
• ODBC connections retrieve controlled extracts from relational database tables
• JSON parsing converts nested API responses into analysis-ready variables
• API request parameters stored in configuration files and documented logs
• Source provenance recorded through retrieval dates, identifiers, and transformation notes

Participants can review Stata code against maintainability, accuracy and reproducibility criteria.

• Code review criteria covering naming, dependencies, side effects, and readability
• Test datasets designed for boundary conditions, missing values, and duplicates
• Unit-style tests verify expected calculations using assert and stored results
• Regression tests compare outputs after code, data, or Stata updates
• README files document project setup, inputs, outputs, assumptions, and execution order
• Git commits, branches, and pull requests support controlled code changes

Participants can translate a business or research question into a programmed Stata analysis specification.

• Analytical questions converted into estimands, datasets, model specifications, and outputs
• Firm-level accounting data mapped to profitability, liquidity, leverage, and growth measures
• Loan portfolio analysis structured around default outcomes, cohorts, and risk drivers
• Market-return datasets aligned with benchmark indices, events, and trading calendars
• Economic indicators joined through common geography, period, unit, and frequency definitions
• Analysis plans define assumptions, validation checks, outputs, and review responsibilities

Participants can build, review and present a complete reproducible Stata analysis from source data to decision-ready outputs.

• Project brief specifies decision question, data sources, methods, and deliverables
• Programmed pipeline imports, validates, transforms, estimates, and exports results
• Econometric specification justified through assumptions, diagnostics, and sensitivity checks
• Reusable program components documented with syntax, options, and returned results
• Automated tables and figures communicate findings for technical and managerial audiences
• Peer review assesses reproducibility, code quality, analytical validity, and presentation clarity