Financial Econometrics Using Stata Training Course

Participants can construct reliable security-level and firm-level datasets from multiple financial sources.

• Define firm, security, accounting, and trading-date identifiers before merging
• Audit duplicate keys using isid, duplicates report, and tagged observations
• Merge price, return, accounting, and benchmark files with merge diagnostics
• Reshape wide vendor extracts into long security-date panel structures
• Construct lagged variables after confirming chronological sort order
• Document sample screens, survivorship choices, and corporate-action treatment

Participants can create standard financial variables and verify their economic meaning.

• Calculate simple, logarithmic, holding-period, and annualised investment returns
• Construct excess returns using risk-free rates and matched frequencies
• Apply stock split adjustments and inspect dividend treatment choices
• Generate market capitalisation, book-to-market, leverage, and profitability measures
• Winsorise extreme observations while retaining a documented original-data field
• Use egen functions for grouped statistics, ranks, and portfolio classifications

Participants can profile financial datasets and communicate distributional and time-series features.

• Summarise central tendency, dispersion, skewness, kurtosis, and percentile measures
• Inspect return distributions using histograms, quantile plots, and kernel densities
• Plot prices, returns, spreads, and ratios against relevant dates
• Compare portfolio characteristics using grouped tables and confidence intervals
• Identify outliers, stale prices, data gaps, and implausible observations
• Export publication-ready graphs and tables using reproducible Stata commands

Participants can apply hypothesis tests and confidence intervals appropriate to financial data.

• Formulate null hypotheses for returns, mean differences, and financial ratios
• Use one-sample and two-sample t-tests with stated variance assumptions
• Apply nonparametric rank-sum tests for non-normal return distributions
• Interpret confidence intervals, p-values, economic magnitude, and statistical significance
• Test variance equality before comparing portfolio or firm-group means
• Control false discoveries when testing multiple investment signals simultaneously

Participants can estimate and interpret baseline regression models for financial relationships.

• Specify dependent variables, explanatory variables, controls, and expected coefficient signs
• Estimate ordinary least squares models using regress and factor-variable notation
• Interpret coefficients, intercepts, residual variation, and adjusted R-squared values
• Create interaction terms for conditional return and performance relationships
• Use margins and marginsplot to interpret nonlinear and interacted specifications
• Produce regression tables with defined samples, units, and significance notation

Participants can diagnose common regression problems and select defensible corrections.

• Inspect residual plots for functional-form errors and influential observations
• Test heteroskedasticity using Breusch-Pagan and White diagnostic procedures
• Apply heteroskedasticity-consistent standard errors to cross-sectional financial regressions
• Measure multicollinearity using variance inflation factors and correlation matrices
• Identify high-leverage observations using hat values, Cook's distance, and dfbeta
• Compare model specifications using economic rationale, diagnostics, and information criteria

Participants can prepare financial time series and test whether standard modelling assumptions hold.

• Declare trading-date series using tsset and verify frequency gaps
• Generate lags, leads, differences, growth rates, and rolling statistics
• Distinguish price-level, return, yield, spread, and ratio time-series properties
• Test unit roots using augmented Dickey-Fuller and Phillips-Perron procedures
• Interpret stationarity results alongside visual evidence and economic reasoning
• Address non-trading days, calendar mismatches, and irregular reporting intervals

Participants can estimate autoregressive models and evaluate financial forecast quality.

• Inspect autocorrelation and partial autocorrelation functions for model identification
• Estimate autoregressive, moving-average, and ARIMA models using arima
• Select lag orders using AIC, BIC, residual diagnostics, and parsimony
• Test residual autocorrelation with Ljung-Box and Breusch-Godfrey procedures
• Generate dynamic forecasts with confidence intervals and forecast origins
• Compare forecast accuracy using MAE, RMSE, bias, and directional accuracy

Participants can analyse portfolio performance against market and policy benchmarks.

• Construct equal-weighted and value-weighted portfolio return series correctly
• Calculate cumulative returns, drawdowns, tracking error, and information ratios
• Align portfolio, benchmark, and risk-free series on matching dates
• Estimate active return relationships using benchmark-relative regression specifications
• Separate arithmetic average returns from geometric compounded performance measures
• Report performance periods, rebalancing assumptions, fees, and missing-price treatment

Participants can estimate and interpret factor exposures for securities and portfolios.

• Specify CAPM regressions using excess asset and market returns
• Estimate alpha, beta, factor loadings, and residual risk measures
• Incorporate Fama-French size, value, profitability, and investment factors
• Test joint factor significance using Wald tests and coefficient restrictions
• Compare single-factor and multi-factor model explanatory performance
• Interpret alpha cautiously after fees, estimation error, and data-mining risk

Participants can design an event study and estimate abnormal-return effects transparently.

• Define event dates, estimation windows, event windows, and exclusion rules
• Match issuer events with trading calendars and benchmark return series
• Estimate market-model expected returns over pre-event estimation periods
• Calculate abnormal returns and cumulative abnormal returns by security
• Test event-window effects using parametric and nonparametric procedures
• Document confounding events, thin trading, and event-date uncertainty

Participants can structure firm-year and security-month panels for econometric analysis.

• Declare panel identifiers and time variables using xtset commands
• Distinguish balanced panels from unbalanced panels and missing observations
• Create within-firm lags, growth measures, and peer-group benchmarks
• Assess variation within entities and across entities before model selection
• Recognise survivorship bias, look-ahead bias, and reporting-date alignment errors
• Build panel samples with transparent inclusion, exclusion, and data-availability rules

Participants can estimate panel models and justify their treatment of unobserved effects.

• Estimate pooled OLS, fixed-effects, and random-effects financial panel models
• Interpret within-entity coefficients and time-invariant variable limitations
• Use Hausman tests alongside institutional reasoning for estimator selection
• Include time fixed effects for common market and macroeconomic shocks
• Cluster standard errors by firm, industry, or calendar period
• Assess serial correlation and cross-sectional dependence in panel residuals

Participants can estimate and assess binary-response models for financial risk outcomes.

• Define default, downgrade, covenant breach, and distress outcome variables
• Estimate logit and probit models using financial and market predictors
• Interpret marginal effects rather than raw nonlinear model coefficients
• Assess classification performance using sensitivity, specificity, and ROC curves
• Select probability thresholds using business costs and risk appetite
• Identify rare-event limitations, class imbalance, and sample-selection concerns

Participants can estimate conditional volatility models and interpret their risk implications.

• Test return residuals for ARCH effects before volatility model estimation
• Estimate ARCH, GARCH, and asymmetric volatility specifications in Stata
• Interpret persistence, mean reversion, and conditional variance forecasts
• Compare normal and Student-t error distributions for financial returns
• Generate volatility forecasts over defined risk-reporting horizons
• Check standardised residuals for remaining autocorrelation and heteroskedasticity

Participants can calculate market-risk measures and assess their performance against realised losses.

• Calculate historical, parametric, and model-based value at risk estimates
• Estimate expected shortfall from return distributions and tail observations
• Set confidence levels, holding periods, and portfolio valuation conventions
• Backtest value at risk exceptions using Kupiec coverage tests
• Analyse exception clustering using Christoffersen independence testing procedures
• Present risk results with assumptions, limitations, and escalation thresholds

Participants can validate financial econometric work and report conclusions suitable for review.

• Separate in-sample fitting, validation samples, and out-of-sample testing periods
• Apply rolling and expanding windows for financial model validation
• Compare nested models using likelihood-ratio tests and information criteria
• Record variable definitions, source files, transformations, and code versions
• Write findings that distinguish association, prediction, and causal claims
• Prepare decision-focused tables, coefficient charts, and model limitation statements

Participants can build, review and present a complete Stata-based financial econometrics analysis addressing a defined business question.

• Frame a finance question with measurable outcomes and decision relevance
• Prepare source data, construct variables, and document sample-selection decisions
• Estimate justified regression, panel, event-study, or volatility model specifications
• Apply diagnostic tests and correct inference for identified data problems
• Present reproducible tables, charts, findings, limitations, and recommended actions
• Review peer analysis against reproducibility, validity, and reporting criteria