Training course

Overview

Advanced Econometrics with R is a comprehensive 5-day professional training course designed for analysts, economists, researchers, statisticians, financial professionals, and quantitative practitioners who need to apply sophisticated econometric methods using R. The programme builds on fundamental regression and statistical concepts and progresses into advanced causal inference, panel-data methods, nonlinear estimation, time series econometrics, forecasting, volatility modelling, and advanced model validation. Participants will develop the ability to construct rigorous empirical models, evaluate identification strategies, diagnose complex modelling problems, and produce reproducible econometric analysis using modern R-based analytical workflows.

The course emphasizes advanced econometric reasoning alongside practical implementation using R and its statistical programming ecosystem. Participants will work with tools and packages such as tidyverse, ggplot2, fixest, plm, AER, sandwich, lmtest, broom, modelsummary, MASS, survival, forecast, fable, vars, and other appropriate packages depending on the analytical application. Advanced regression inference, robust and clustered standard errors, specification testing, endogeneity, instrumental variables, generalized method of moments, fixed effects, random effects, treatment effects, difference-in-differences, event studies, and regression discontinuity concepts are integrated into practical analytical workflows.

The programme progresses into advanced time series and nonlinear econometrics, covering stationarity, unit roots, structural breaks, cointegration, error correction models, vector autoregression, Granger causality, impulse-response analysis, ARIMA, state-dependent relationships, ARCH/GARCH volatility models, limited dependent variables, maximum likelihood estimation, count models, censored outcomes, sample selection, and nonlinear effects. Practical case studies focus on realistic economic, financial, business, policy, market, and organizational questions, while exercises require participants to compare model specifications, assess identification assumptions, conduct robustness checks, and interpret uncertainty rather than relying solely on statistical significance.

By the end of the Advanced Econometrics with R course, participants will be able to design, estimate, diagnose, validate, document, and communicate sophisticated econometric models using R. The programme incorporates established econometric frameworks, reproducible research principles, transparent analytical assumptions, robustness and sensitivity analysis, model comparison, appropriate inference, and professional reporting practices. An integrated capstone enables participants to apply advanced econometric techniques to a real-world dataset, develop a defensible analytical strategy, validate the resulting models, and communicate findings through reproducible code, statistical tables, visualizations, and a professional empirical report.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Economists, econometricians, statisticians, and quantitative researchers

• Advanced data analysts and business intelligence professionals

• Financial, investment, and risk analysts working with complex datasets

• Academic researchers and postgraduate students conducting empirical research

• Policy analysts and development-sector research professionals

• Monitoring, evaluation, and impact assessment specialists

• Market research and economic intelligence professionals

• Professionals conducting advanced causal, panel, or time series analysis

• R users seeking advanced econometric modelling and statistical programming skills

• Analysts and researchers who have prior exposure to regression analysis and want advanced practical econometrics capabilities

Course Objectives

By the end of the training, participants will be able to:

• Design advanced econometric strategies for complex empirical research questions

• Develop efficient and reproducible R workflows for advanced econometric analysis

• Apply advanced regression methods and robust inference techniques

• Diagnose model specification problems and assess the sensitivity of econometric conclusions

• Identify and address common sources of endogeneity and selection bias

• Apply instrumental variables, two-stage least squares, and generalized method of moments techniques

• Estimate and interpret advanced panel-data models using fixed effects, random effects, and related approaches

• Apply difference-in-differences, event-study, and regression-discontinuity concepts appropriately

• Evaluate treatment effects, heterogeneous effects, placebo tests, and identification assumptions

• Estimate nonlinear and limited dependent variable models using maximum likelihood methods

• Apply logit, probit, ordered, multinomial, count, censored, truncated, and sample-selection models

• Analyse stationarity, unit roots, structural breaks, and dynamic relationships in time series data

• Apply cointegration, error correction, VAR, Granger causality, and impulse-response techniques

• Build and evaluate ARIMA and other advanced forecasting models

• Apply ARCH and GARCH methods to analyse conditional volatility and financial risk

• Conduct robustness checks, sensitivity analysis, model comparison, and alternative specifications

• Develop reusable R functions and structured workflows for advanced econometric projects

• Produce reproducible research using R Projects, R Markdown or Quarto, documentation, and version control practices

• Communicate advanced econometric findings through professional tables, visualizations, technical reports, and evidence-based conclusions

Course Content

Day 1: Advanced Regression, Inference, Identification, and Model Specification

Module 1: Advanced Regression Econometrics and Robust Empirical Modelling

1.      Advanced Econometric Research Design, Identification Strategies, and Model-Building Frameworks

2.      Advanced R Econometric Workflows, Packages, Model Objects, Tidy Outputs, and Reproducible Projects

3.      Robust, Heteroskedasticity-Consistent, Clustered, and Multi-Way Clustered Inference

4.      Advanced Functional Forms, Transformations, Interactions, Nonlinear Effects, and Marginal Relationships

5.      Model Specification, Omitted Variables, Measurement Error, Functional Misspecification, and Specification Tests

6.      Multicollinearity, Influential Observations, Leverage, Outliers, Residual Diagnostics, and Model Stability

7.      Generalized Least Squares, Weighted Estimation, Feasible GLS, and Efficiency Considerations

8.      Model Selection, Information Criteria, Predictive Performance, Cross-Validation, and Out-of-Sample Assessment

9.      Case Study: Building and Stress-Testing an Advanced Economic, Financial, or Business Performance Model

10.  Practical Exercise: Comparing Alternative Specifications, Diagnosing Model Problems, and Defending an Advanced Regression Strategy in R

Day 2: Advanced Panel Data and Causal Econometrics

Module 2: Advanced Causal Identification, Panel Models, and Treatment Effects

1.      Advanced Causal Inference, Counterfactual Frameworks, Identification Assumptions, and Research Design

2.      Endogeneity, Confounding, Reverse Causality, Measurement Error, Selection Bias, and Identification Failure

3.      Instrumental Variables, Two-Stage Least Squares, Instrument Strength, and Exogeneity Assessment

4.      Generalized Method of Moments, Moment Conditions, Weighting Matrices, and Robust GMM Inference

5.      Advanced Panel Data Structures, Within and Between Variation, Time Effects, and Individual Heterogeneity

6.      Fixed Effects, Random Effects, Correlated Random Effects, and High-Dimensional Fixed Effects

7.      Dynamic Panel Models, Lagged Dependent Variables, Serial Dependence, and Endogenous Regressors

8.      Difference-in-Differences, Event Studies, Parallel Trends, Treatment Effects, and Heterogeneous Effects

9.      Case Study: Evaluating a Policy, Investment, Technology Adoption, Training Programme, or Market Intervention

10.  Practical Exercise: Designing, Estimating, Testing, and Validating an Advanced Causal Panel Model in R

Day 3: Nonlinear Econometrics, Maximum Likelihood, and Limited Dependent Variables

Module 3: Advanced Nonlinear Modelling, MLE, and Discrete Outcomes

1.      Nonlinear Econometric Relationships, Model Structure, Marginal Effects, and Interpretation

2.      Maximum Likelihood Estimation, Likelihood Functions, Numerical Optimization, and Estimation Diagnostics

3.      Binary Response Models, Logit, Probit, Odds Ratios, Predicted Probabilities, and Marginal Effects

4.      Ordered and Multinomial Response Models for Ranked and Multiple-Category Outcomes

5.      Count Data Models, Poisson Regression, Negative Binomial Models, and Overdispersion

6.      Censored and Truncated Regression, Tobit Models, and Limited Outcome Processes

7.      Sample Selection Models, Selection Bias, Inverse Mills Ratio Concepts, and Practical Applications

8.      Nonlinear Models with Interactions, Heterogeneous Effects, Predictions, and Counterfactual Analysis

9.      Case Study: Modelling Customer Choices, Employment Outcomes, Credit Decisions, Participation, Demand, or Event Counts

10.  Practical Exercise: Estimating, Diagnosing, Comparing, and Interpreting Advanced Nonlinear Models Using R

Day 4: Advanced Time Series, Cointegration, Volatility, and Dynamic Systems

Module 4: Advanced Dynamic Econometrics and Time Series Modelling

1.      Advanced Time Series Foundations, Dynamic Dependence, Trends, Seasonality, and Structural Dynamics

2.      Stationarity, Unit Roots, Integration Orders, Deterministic Components, and Spurious Regression

3.      Augmented Dickey-Fuller, Phillips-Perron, KPSS, and Complementary Stationarity Diagnostics

4.      Structural Breaks, Regime Changes, Stability Testing, and Break-Aware Econometric Modelling

5.      ARIMA, Seasonal Models, Dynamic Regression, Forecasting, and Advanced Prediction Evaluation

6.      Cointegration, Engle-Granger Procedures, Johansen Methods, and Long-Run Equilibrium Relationships

7.      Error Correction Models, Short-Run Dynamics, Long-Run Effects, and Adjustment Mechanisms

8.      Vector Autoregression, Granger Causality, Impulse Responses, Forecast Error Variance Decomposition, and Dynamic Systems

9.      ARCH and GARCH Models, Conditional Variance, Volatility Persistence, Asymmetry, and Financial Risk Applications

10.  Case Study and Practical Exercise: Building, Validating, and Interpreting an Advanced Dynamic Econometric System in R

Day 5: Advanced R Econometrics, Robustness, Reproducibility, and Capstone

Module 5: Advanced Econometric Practice, Validation, Reporting, and Capstone

1.      Advanced Model Validation, Specification Comparison, Sensitivity Analysis, and Robustness Frameworks

2.      Placebo Tests, Falsification Strategies, Negative Controls, and Alternative Identification Checks

3.      Heterogeneous Treatment Effects, Subgroup Analysis, Interaction-Based Effects, and Distributional Considerations

4.      Advanced Post-Estimation Analysis, Predictions, Marginal Effects, Counterfactuals, and Model Visualization

5.      Building Reusable R Econometric Functions, Pipelines, Automation, and Structured Analytical Workflows

6.      Reproducible Research Using R Projects, R Markdown or Quarto, Package Management, Documentation, and Version Control

7.      Professional Econometric Reporting Using broom, modelsummary, gt, ggplot2, and Reproducible Statistical Tables and Visualizations

8.      Analytical Governance, Research Transparency, Data Provenance, Model Risk, Documentation, and Responsible Interpretation

9.      Capstone Case Study: End-to-End Advanced Econometric Research from Research Design and Raw Data to Validated R Models and Professional Reporting

10.  Capstone Exercise: Conducting, Defending, Stress-Testing, Documenting, and Presenting a Complete Advanced Econometric Analysis Using R