Applied Econometrics for Finance Training Course

Participants can prepare documented datasets suitable for econometric estimation and review.

• Market, central-bank and official-statistics data sources assessed
• Frequency alignment across daily prices, monthly indicators and quarterly accounts
• Missing observations treated using documented economic and statistical rules
• Outliers investigated before winsorisation, deletion or transformation decisions
• Nominal series converted using price deflators and appropriate base periods
• Data dictionary records units, transformations, sources and revision dates

Participants can profile distributions, relationships and structural features before modelling.

• Summary statistics interpreted for returns, yields, spreads and macro series
• Distribution shape assessed using histograms, quantiles, skewness and kurtosis
• Time plots reveal trends, seasonality, breaks and volatility clustering
• Scatterplots distinguish linear association from economically implausible relationships
• Correlation matrices screened for redundant predictors and common shocks
• Transformations selected using distributional evidence and economic interpretation

Participants can estimate and interpret ordinary least squares models for financial questions.

• Ordinary least squares assumptions linked to unbiased coefficient estimation
• Regression coefficients interpreted using units, ceteris paribus effects and signs
• Intercepts, slopes and fitted values explained for decision audiences
• R-squared and adjusted R-squared used without overstating model quality
• Confidence intervals distinguish estimated effects from sampling uncertainty
• Economic significance compared with statistical significance before recommendations

Participants can specify multivariable finance models using theory and diagnostic evidence.

• Control variables selected from economic theory and transmission mechanisms
• Functional forms compared: levels, logs, growth rates and percentage changes
• Interaction terms test conditional effects across market regimes
• Dummy variables represent policy changes, crises and calendar effects
• Lag structures capture delayed responses in macroeconomic and financial series
• Parsimony balanced against omitted-variable risk and interpretability requirements

Participants can identify and address common threats to regression validity.

• Residual plots detect nonlinearity, heteroskedasticity and influential observations
• Variance inflation factors identify problematic multicollinearity among predictors
• Breusch-Pagan testing assesses heteroskedastic error variance
• Durbin-Watson statistics flag residual autocorrelation in ordered data
• Ramsey RESET testing examines functional-form misspecification
• Heteroskedasticity-consistent standard errors improve inference under unequal variance

Participants can test financial hypotheses and compare competing regression specifications.

• Null and alternative hypotheses stated in economically meaningful terms
• t-tests assess individual coefficient restrictions and factor exposures
• F-tests compare nested models and joint policy restrictions
• p-values interpreted alongside effect size, uncertainty and prior evidence
• Information criteria guide selection between non-nested candidate models
• Out-of-sample tests separate explanatory fit from forecasting performance

Participants can design forecasts and evaluate their accuracy against relevant benchmarks.

• Forecast horizons aligned with treasury, planning and investment decisions
• Training, validation and test samples separated without look-ahead bias
• Naive and random-walk benchmarks established before complex forecasting models
• Mean absolute error and RMSE compared across candidate forecasts
• Directional accuracy assessed for rates, returns and market indicators
• Forecast intervals communicate uncertainty around central projections

Participants can assess stationarity and transform time series appropriately before modelling.

• Stationarity distinguished from deterministic trends and structural breaks
• Autocorrelation and partial autocorrelation functions diagnose serial dependence
• Augmented Dickey-Fuller tests assess unit roots in economic series
• Phillips-Perron testing provides an alternative unit-root assessment
• First differences, log differences and returns selected appropriately
• Spurious regression risks identified when nonstationary series are combined

Participants can build and evaluate ARIMA models for univariate forecasting.

• Box-Jenkins identification uses autocorrelation and partial autocorrelation patterns
• AR, MA and ARIMA terms matched to observed serial dependence
• Seasonal ARIMA specifications address recurring calendar-driven movements
• Maximum likelihood estimation produces candidate time-series models
• Residual whiteness checked using Ljung-Box portmanteau testing
• Rolling forecasts evaluated against benchmark and holdout performance

Participants can model dynamic relationships among macroeconomic and financial variables.

• Vector autoregression structure models jointly determined economic variables
• Lag length selected using AIC, BIC and residual diagnostics
• Granger causality tests assess incremental predictive content
• Impulse response functions trace shocks through interconnected variables
• Forecast error variance decomposition attributes uncertainty to shocks
• Ordering assumptions justified when identifying structural economic shocks

Participants can model long-run equilibrium relationships between nonstationary financial series.

• Cointegration distinguishes stable long-run relationships from spurious correlation
• Engle-Granger testing assesses cointegration between two integrated series
• Johansen testing identifies multiple cointegrating relationships within systems
• Error-correction terms quantify adjustment toward long-run equilibrium
• Vector error-correction models combine short-run dynamics and equilibrium correction
• Yield curves, purchasing power parity and spread relationships interpreted

Participants can estimate conditional volatility models for financial risk and forecasting.

• Volatility clustering identified from squared returns and residual diagnostics
• ARCH effects tested using Engle's Lagrange multiplier procedure
• GARCH models estimate time-varying conditional variance
• Asymmetric GARCH specifications capture differing responses to negative shocks
• Volatility forecasts converted into risk limits and scenario inputs
• Standardised residuals checked for remaining serial dependence and ARCH effects

Participants can assess model-based market risk estimates using formal backtesting procedures.

• Value at Risk estimated from parametric and historical approaches
• Expected shortfall interpreted for tail-loss reporting and capital discussions
• Conditional volatility models supply inputs to market-risk calculations
• Kupiec backtesting compares observed exceptions with expected exceedances
• Christoffersen testing examines exception independence and coverage accuracy
• Stress scenarios complement statistical risk measures during structural breaks

Participants can estimate panel models for firms, banks, countries or securities.

• Panel structure combines cross-sectional variation with repeated observations
• Pooled OLS compared with fixed-effects and random-effects specifications
• Fixed effects control for time-invariant unobserved entity characteristics
• Hausman testing informs fixed-effects versus random-effects model selection
• Time effects capture common monetary, regulatory and market shocks
• Clustered standard errors address within-entity error dependence

Participants can assess causal claims using appropriate quasi-experimental methods.

• Causal diagrams clarify confounders, mediators and selection mechanisms
• Difference-in-differences estimates policy effects using treated and comparison groups
• Parallel-trends assumptions tested using pre-treatment outcome patterns
• Instrumental variables address endogenous explanatory variables under valid exclusion restrictions
• Regression discontinuity exploits assignment thresholds for local causal estimates
• Causal claims bounded by design assumptions and sensitivity analysis

Participants can assess the market impact of events and estimate factor exposures.

• Event windows defined around announcements, policy actions and corporate disclosures
• Market models estimate expected returns before event-date abnormal returns
• Cumulative abnormal returns measure multi-day market reaction
• Estimation windows separated from events to avoid contamination
• CAPM and Fama-French factors estimate systematic return exposures
• Cross-sectional tests compare event effects across firms and sectors

Participants can document, challenge and communicate econometric findings for financial decisions.

• Model documentation records purpose, data, assumptions and intended use
• Reproducible workflows preserve code, inputs, outputs and version history
• Independent review checks specification choices, diagnostics and interpretation
• Limitations stated clearly: sample dependence, endogeneity and regime change
• Executive charts present forecasts, uncertainty ranges and decision implications
• Model-monitoring triggers specify recalibration, review and retirement conditions

Participants can build, review and present a complete econometric analysis addressing a finance or macroeconomic decision.

• Decision question scoped with measurable outcome and economic rationale
• Dataset assembled, transformed and documented using reproducible procedures
• Competing econometric specifications estimated against stated identification assumptions
• Diagnostics, robustness checks and forecast tests recorded systematically
• Findings translated into financial implications, uncertainty and recommended actions
• Executive presentation defended through peer review and technical challenge