Training course

Overview

Econometrics with R is a comprehensive 5-day professional training course designed to provide participants with practical skills for conducting, interpreting, and communicating econometric analysis using the R programming environment. The course combines core econometric theory with hands-on statistical programming, enabling participants to analyse economic, financial, business, operational, market, and policy data using reproducible R workflows. Participants will progress from data preparation and exploratory analysis to regression modelling, statistical inference, diagnostics, causal analysis, panel data, time series econometrics, forecasting, and advanced econometric applications.

The programme begins with the foundations of applied econometrics and the R ecosystem, including R and RStudio, R scripts, R Markdown, packages, data structures, data import, cleaning, transformation, visualization, and exploratory analysis. Participants will use practical tools such as tidyverse, dplyr, ggplot2, readr, and related packages to prepare and explore real-world datasets before estimating econometric models. The course introduces the classical linear model framework, ordinary least squares, hypothesis testing, confidence intervals, model specification, functional forms, categorical variables, interaction effects, and robust inference, while emphasizing data quality, reproducibility, transparent assumptions, and professional analytical practices.

The course then progresses into applied causal inference, endogeneity, instrumental variables, panel data, fixed and random effects, difference-in-differences, time series econometrics, stationarity, unit roots, ARIMA, cointegration, vector autoregression, and volatility modelling. Participants will learn how R packages and modelling workflows can be used to estimate, diagnose, validate, compare, and communicate econometric models. Practical case studies and exercises address real-world analytical questions involving prices, demand, sales, productivity, employment, investment, financial indicators, policy interventions, and economic performance, helping participants connect econometric methods with practical research and decision-making requirements.

By the end of the Econometrics with R course, participants will be able to develop reproducible R-based econometric workflows from raw data through model estimation, diagnostics, robustness analysis, interpretation, and reporting. The course emphasizes established econometric principles, appropriate statistical inference, causal identification, model validation, sensitivity analysis, reproducible research, and clear communication of quantitative evidence. Through practical exercises and an applied capstone project, participants will integrate R programming, econometric modelling, visualization, diagnostics, and professional reporting to solve realistic economic, financial, business, and policy analysis problems.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Economists, statisticians, and quantitative analysts

• Data analysts and business intelligence professionals

• Financial, investment, and risk analysts

• Researchers conducting economic, financial, business, or policy studies

• Academic researchers, lecturers, and postgraduate students

• Monitoring, evaluation, and impact assessment professionals

• Policy analysts and development-sector professionals

• Market research and commercial analytics specialists

• Professionals transitioning from spreadsheet-based analysis to R-based econometric modelling

• Analysts and researchers seeking practical competence in applied econometrics using R

Course Objectives

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

• Explain the principles, purpose, and workflow of applied econometric analysis

• Navigate R and RStudio and establish reproducible econometric projects

• Import, clean, transform, merge, reshape, and validate datasets using R

• Conduct descriptive statistics, correlation analysis, exploratory analysis, and data visualization

• Use tidyverse, dplyr, ggplot2, and related R packages for practical data analysis

• Build and estimate simple and multiple linear regression models

• Interpret coefficients, standard errors, confidence intervals, hypothesis tests, and goodness-of-fit measures

• Apply the classical linear model framework and evaluate key econometric assumptions

• Diagnose heteroskedasticity, multicollinearity, autocorrelation, specification problems, and influential observations

• Apply robust and clustered inference where appropriate

• Distinguish correlation, prediction, and causal relationships in empirical analysis

• Apply instrumental variables and two-stage least squares to appropriate endogeneity problems

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

• Apply difference-in-differences and related causal evaluation techniques

• Conduct time series analysis involving stationarity, unit roots, ARIMA, cointegration, and dynamic models

• Develop, evaluate, and communicate econometric forecasts using R

• Apply advanced methods involving limited dependent variables, nonlinear models, and volatility

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

• Build reproducible workflows using R scripts, R Markdown or Quarto, project structures, and documented code

• Present econometric findings through professional tables, visualizations, reports, and evidence-based conclusions

Course Content

Day 1: Econometric Foundations, R Environment, Data Preparation, and Regression

Module 1: R Foundations, Applied Data Management, and Econometric Modelling

1.      Introduction to Econometrics, Empirical Research Questions, and the Applied Econometric Workflow

2.      R and RStudio Environment, R Projects, Scripts, Packages, Documentation, and Reproducible Workflows

3.      R Data Structures, Data Frames, Tibbles, Variables, Factors, Dates, and Missing Values

4.      Importing, Cleaning, Transforming, Merging, Reshaping, and Validating Econometric Data

5.      Descriptive Statistics, Frequency Analysis, Correlation, Group Summaries, and Exploratory Data Visualization

6.      Using dplyr, tidyr, readr, and ggplot2 for Practical Econometric Data Preparation and Exploration

7.      Defining Dependent and Independent Variables, Hypotheses, Economic Relationships, and Model Specifications

8.      Simple and Multiple Linear Regression Using R and the Ordinary Least Squares Framework

9.      Case Study: Analysing Prices, Sales, Demand, Costs, Productivity, and Business Performance Using R

10.  Practical Exercise: Preparing a Real-World Dataset and Estimating and Interpreting an Initial Econometric Model

Day 2: Regression Inference, Diagnostics, and Model Improvement

Module 2: Applied Regression Analysis, Statistical Inference, and Econometric Diagnostics

1.      Classical Linear Model Assumptions and the Gauss-Markov Framework

2.      Hypothesis Testing, Standard Errors, Confidence Intervals, t-Tests, F-Tests, and Joint Hypotheses

3.      Statistical Significance, Economic Significance, Effect Sizes, and Practical Interpretation

4.      Functional Forms, Logarithmic Models, Elasticities, Growth Rates, and Nonlinear Relationships

5.      Factor Variables, Dummy Variables, Interaction Terms, and Group-Specific Effects in R

6.      Multicollinearity, Variance Inflation, Variable Selection, and Model Specification Considerations

7.      Heteroskedasticity, Breusch-Pagan Testing, Robust Standard Errors, and Appropriate Inference

8.      Autocorrelation, Residual Analysis, Influence, Leverage, Outliers, and Specification Diagnostics

9.      Case Study: Improving a Regression Model for Productivity, Customer Behaviour, Investment, or Operational Performance

10.  Practical Exercise: Diagnosing, Revising, Validating, and Interpreting a Complete Regression Model in R

Day 3: Causal Inference, Endogeneity, and Panel Data with R

Module 3: Applied Causal Analysis, Instrumental Variables, and Panel Econometrics

1.      Correlation versus Causation, Counterfactual Reasoning, Identification, and Causal Questions

2.      Endogeneity, Omitted Variable Bias, Reverse Causality, Measurement Error, and Selection Problems

3.      Instrumental Variables, Relevance, Exogeneity, and Two-Stage Least Squares Using R

4.      Instrument Diagnostics, Weak Instruments, Overidentification, and Robust Causal Inference

5.      Panel Data Structures, Individual Effects, Time Effects, Within Variation, and R Panel Workflows

6.      Fixed-Effects and Random-Effects Models, Model Selection, and Interpretation of Panel Estimates

7.      Clustered Standard Errors, Serial Correlation, Heterogeneity, and Panel Model Diagnostics

8.      Difference-in-Differences, Treatment Effects, Parallel Trends, and Intervention Evaluation

9.      Case Study: Measuring the Impact of a Policy, Training Programme, Investment, Technology Adoption, or Operational Intervention

10.  Practical Exercise: Estimating and Interpreting a Causal Panel-Data Model with Robustness Checks in R

Day 4: Time Series Econometrics, Forecasting, and Dynamic Models

Module 4: Applied Time Series Analysis, Forecasting, and Dynamic Econometrics

1.      Time Series Data, Trends, Seasonality, Cycles, Serial Dependence, and Dynamic Economic Relationships

2.      Time-Based Data Structures in R, Lag Operators, Transformations, Growth Rates, and Time Series Visualization

3.      Stationarity, Unit Roots, Random Walks, and the Risks of Spurious Regression

4.      Augmented Dickey-Fuller Testing, Differencing, Trend Treatment, and Stationarity Diagnostics

5.      Autoregressive Models, Moving Average Models, ARIMA, and Practical Forecasting with R

6.      Distributed Lags, Dynamic Regression, Short-Run Effects, and Long-Run Relationships

7.      Cointegration, Error Correction Models, and Analysis of Long-Run Economic Relationships

8.      Vector Autoregression, Granger Causality, Impulse Responses, and Dynamic Interactions

9.      Case Study: Forecasting Sales, Demand, Prices, Exchange Rates, Inflation, or Financial Indicators Using R

10.  Practical Exercise: Building, Validating, and Interpreting a Time Series Econometric Forecasting Model

Day 5: Advanced Econometrics with R, Reproducibility, Reporting, and Capstone

Module 5: Advanced Applied Econometrics, Professional R Workflows, and Capstone

1.      Limited Dependent Variable Models, Logit, Probit, Ordered Outcomes, and Marginal Effects

2.      Count Models, Censored and Truncated Outcomes, Sample Selection, and Practical Model Interpretation

3.      Maximum Likelihood Estimation, Nonlinear Models, Model Comparison, and Advanced Post-Estimation Analysis

4.      ARCH and GARCH Models, Volatility Dynamics, and Financial Risk Applications in R

5.      Structural Breaks, Regime Changes, Model Stability, and Advanced Time Series Diagnostics

6.      Advanced R Econometric Workflows Using Functions, Iteration, Model Objects, Tidy Outputs, and Reusable Code

7.      Robustness Checks, Sensitivity Analysis, Alternative Specifications, Model Selection, and Validation Strategies

8.      Reproducible Econometric Research Using R Projects, R Markdown or Quarto, Version Control, Documentation, and Data Provenance

9.      Case Study: End-to-End Econometric Analysis from Raw Data to Validated R Model, Interpretation, Visualization, and Professional Report

10.  Capstone Exercise: Conducting, Validating, Documenting, and Presenting a Complete Applied Econometric Analysis Using R