R Econometrics for Applied Economic Data Analysis Training Course

Course overview

Economic analysts are routinely asked to explain what changed, why it changed, and how confident decision-makers should be in the evidence. A spreadsheet regression or a standard R output table is rarely enough when data contain missing values, serial correlation, policy shocks, selection effects, clustered observations, or unobserved firm and regional differences. This course equips participants to build defensible econometric analyses in R, diagnose weak model assumptions, and communicate results that can withstand review by finance leaders, economists, auditors, and policy stakeholders.

Participants work through an applied R workflow for importing, cleaning, structuring, modelling, testing, and reporting economic data. They use tidyverse tools for data preparation, estimate OLS, fixed-effects, instrumental-variables, difference-in-differences, and time-series models, and apply robust and clustered standard errors. The programme covers model specification, multicollinearity, heteroskedasticity, autocorrelation, endogeneity, causal interpretation, coefficient visualisation, and reproducible reporting. By the end of the week, participants can select an appropriate econometric method for a business or economic question rather than relying on a default regression command.

Teaching combines instructor-led econometric explanation with guided coding in RStudio, worked examples, and daily analysis labs using realistic macroeconomic, market, firm-level, and policy datasets. Participants progressively develop an end-to-end applied econometrics project: a documented R script or Quarto report containing cleaned data, exploratory analysis, model estimates, diagnostic tests, charts, and an evidence-based interpretation for a defined decision question. The final workshop includes peer review of modelling choices and a practical plan for applying the workflow to participants' own data.

The course is designed for economists, financial analysts, researchers, and quantitative professionals who already use data in their role and need stronger econometric judgement alongside practical R capability. It is especially valuable where analysis informs investment, pricing, forecasting, policy, risk, performance, or market decisions.

Course objectives

By the end of this course, participants will be able to:

• Prepare and validate economic datasets in R using tidyverse data-wrangling and reshaping workflows
• Estimate and interpret OLS regression models with economically meaningful specifications and transformations
• Diagnose heteroskedasticity, multicollinearity, influential observations, and autocorrelation using R tests and plots
• Apply heteroskedasticity-robust and clustered standard errors to produce defensible inference
• Build fixed-effects and random-effects panel-data models using firm, sector, regional, or time identifiers
• Estimate instrumental-variables and difference-in-differences models for applied causal-analysis questions
• Develop and evaluate time-series regression and forecasting models using stationarity and residual diagnostics
• Produce a reproducible Quarto econometrics report containing code, model tables, charts, diagnostics, and recommendations

Benefits of attending

Target competencies

Who should attend

• Economists — who need to turn economic datasets into defensible causal and forecasting evidence
• Financial Analysts — who evaluate drivers of revenue, margins, asset prices, or investment performance
• Economic Research Analysts — who need reproducible R workflows for empirical research and policy analysis
• Risk Analysts — who model macroeconomic, market, credit, or operational factors affecting risk exposure
• Data Analysts — who need to move beyond descriptive reporting into statistically sound economic modelling
• Policy and Strategy Analysts — who assess the effects of interventions, regulations, pricing, or market changes

Requirements and prerequisites

Participants should be comfortable working with tabular data and should understand core statistical concepts including variables, distributions, correlation, hypothesis tests, confidence intervals, and linear regression coefficients. Prior experience writing basic R code is expected: importing a file, creating objects, using functions, and running a simple script in RStudio. Familiarity with finance, economics, or business data is helpful. Participants should bring a laptop with current R and RStudio installed where permitted. Prior use of panel-data, instrumental-variables, causal-inference, or time-series methods is not required; these methods are taught from first principles before being implemented in R.

Training methodology

Each day alternates focused instructor-led explanation with live RStudio demonstrations and supervised coding labs. Participants work with economic and financial datasets to make modelling decisions, not simply reproduce commands: they inspect data quality, formulate hypotheses, estimate competing models, test assumptions, and interpret limitations. Short case discussions address issues such as evaluating a policy change, explaining firm performance, and forecasting an economic series. Group review sessions compare model specifications and inference choices. The final day is an applied workshop in which participants complete and present a reproducible R analysis plan and report.

Course outline

Day 1: Applied R workflow and regression foundations

• RStudio projects, scripts, packages, and reproducible folder structures
• Importing CSV, Excel, and API-derived economic datasets into R
• Data cleaning with dplyr, tidyr, and explicit missing-data checks
• Exploratory data analysis with summary statistics and ggplot2 graphics
• Economic variable construction, log transformations, rates, and index numbers
• OLS regression estimation with lm() and coefficient interpretation
• Model specification from an economic question and testable hypothesis

Workshop: Participants clean a firm-performance dataset and produce an initial OLS model with a documented variable dictionary and exploratory charts.

Day 2: Regression diagnostics and robust inference

• Residual plots, leverage, influence, and Cook's distance
• Heteroskedasticity testing with Breusch-Pagan and White-style diagnostics
• Multicollinearity assessment using correlation matrices and variance inflation factors
• Functional-form checks, interaction terms, and nonlinear specifications
• Robust standard errors using sandwich estimators
• Clustered standard errors for grouped firms, regions, or repeated observations
• Interpreting confidence intervals, p-values, effect sizes, and economic significance

Workshop: Participants audit a pricing regression, identify specification and inference risks, and issue a revised model table with robust or clustered standard errors.

Day 3: Panel data and causal evaluation

• Longitudinal data structures, panel identifiers, and balance checks
• Pooled OLS versus fixed-effects and random-effects estimators
• Entity and time fixed effects using fixest
• Within-estimator interpretation and time-invariant omitted factors
• Difference-in-differences design, treatment timing, and parallel-trends assessment
• Instrumental-variables logic, relevance, exclusion, and two-stage least squares
• Threats to causal inference including selection bias, reverse causality, and spillovers

Workshop: Participants estimate the effect of a regional policy intervention using a fixed-effects difference-in-differences model and prepare a treatment-effect chart.

Day 4: Time-series econometrics and forecasting

• Time-series indexing, lags, leads, rolling windows, and seasonal features
• Stationarity, unit roots, differencing, and augmented Dickey-Fuller testing
• Autocorrelation diagnostics using ACF, PACF, and Ljung-Box tests
• Dynamic regression models with lagged explanatory variables
• ARIMA and seasonal ARIMA model identification in forecast
• Forecast accuracy measures including MAE, RMSE, and MAPE
• Forecast intervals, structural breaks, and scenario-based interpretation

Workshop: Participants develop and compare forecasting models for an economic indicator, then submit a forecast chart and accuracy comparison for management use.

Day 5: Reporting, review, and workplace application

• Model comparison tables using modelsummary and publication-ready formatting
• Coefficient plots, marginal effects, and uncertainty visualisation
• Quarto reports with executable code, narrative, tables, and figures
• Reproducibility controls using package management and scripted data pipelines
• Communicating causal claims, assumptions, and limitations to non-technical audiences
• Econometric model review checklists for internal challenge and governance
• Translating an organisational decision question into an R econometrics project plan

Workshop: Participants complete a Quarto-based econometric analysis pack and present its findings, model limitations, and next-step recommendation in a peer review session.

Tools & standards covered

R, RStudio, tidyverse, fixest

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions