Econometric Analysis for Economic Policy Analysts Training Course

Course overview

Economic policy analysts are expected to turn imperfect administrative records, surveys, labour-market indicators and macroeconomic series into advice that can withstand challenge from ministers, budget holders, regulators and the public. That requires more than producing a regression table: analysts must define a policy question, select an identification strategy, test whether assumptions are credible, quantify uncertainty and explain what the evidence does—and does not—support. This course addresses the practical gap between statistical output and defensible policy analysis.

Participants learn to structure policy evaluations using descriptive analysis, ordinary least squares regression, panel-data methods, difference-in-differences, instrumental variables and basic forecasting techniques. They will work with cross-sectional, time-series and panel datasets; specify models; diagnose multicollinearity, heteroskedasticity and serial correlation; interpret coefficients, marginal effects and confidence intervals; and translate results into policy-relevant findings. The course also covers data cleaning, variable construction, reproducible workflows, visualisation and the disciplined use of robustness checks.

Delivery combines instructor-led explanation with guided analysis in Stata, R and Excel using realistic public-policy datasets. Cases cover questions such as whether a skills subsidy improved employment outcomes, whether a tax change affected business activity, and how to estimate demand for public services. Participants complete an end-of-course policy evidence pack: a defined evaluation question, analysis plan, cleaned dataset specification, reproducible model output, diagnostic results, charts and a concise policy briefing that states findings, limitations and recommendations.

The programme is designed for analysts who produce, commission or scrutinise quantitative evidence for economic, fiscal, labour, industrial, social or regulatory policy. It is especially valuable where teams need more consistent analytical standards before evidence is used in submissions, impact assessments, spending reviews or programme evaluations.

Course objectives

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

• Frame a policy question as a testable causal or predictive econometric specification
• Prepare cross-sectional, time-series and panel datasets using documented cleaning and variable-construction procedures
• Estimate and interpret ordinary least squares regression models for policy outcomes
• Diagnose heteroskedasticity, multicollinearity, influential observations and serial correlation using formal tests and plots
• Apply fixed-effects, difference-in-differences and instrumental-variables methods to policy evaluation questions
• Construct confidence intervals, hypothesis tests and marginal-effect estimates for decision-ready interpretation
• Produce reproducible tables, coefficient plots and forecast outputs in Stata, R and Excel
• Write a concise policy evidence brief that communicates findings, assumptions, limitations and recommendations

Benefits of attending

Target competencies

Who should attend

• Economic Policy Analysts — who must convert economic data into defensible advice for policy decisions
• Public Finance Analysts — who assess the fiscal effects and value for money of government interventions
• Labour Market Analysts — who evaluate employment, wages, skills and workforce programme outcomes
• Regulatory Impact Analysts — who need credible evidence on behavioural and market effects of regulation
• Programme Evaluation Officers — who design and interpret quantitative evaluations of public initiatives
• Central Bank and Treasury Analysts — who monitor macroeconomic indicators and prepare evidence for fiscal or monetary briefings

Requirements and prerequisites

Participants should be comfortable reading charts and tables, using percentages and rates, and working with structured data in Excel or a similar spreadsheet. Familiarity with basic descriptive statistics—mean, median, variance, correlation and sampling—is helpful, as is an awareness of economic concepts such as inflation, unemployment, GDP, prices or demand. No prior econometrics course, coding experience or advanced mathematics is required. Complete beginners should expect an intensive introduction to regression notation and statistical inference before progressing to applied models. Participants will benefit most if they bring one current policy question or dataset context from their work.

Training methodology

The instructor introduces each method through a policy decision scenario, then demonstrates the workflow in Stata, R or Excel before participants replicate it on supplied datasets. Guided labs cover data preparation, model estimation, diagnostic testing and interpretation of output; short case discussions focus on whether a claimed policy effect is credible. Teams compare alternative specifications and defend their methodological choices. Each day closes with an applied task, building towards an individual policy evidence pack and a practical plan for applying the methods to a live work question.

Course outline

Day 1: From policy questions to analysable evidence

• Policy problems, outcomes, treatment variables and comparison groups
• Correlation, causation and the counterfactual framework
• Cross-sectional, time-series and panel-data structures
• Data dictionaries, unit-of-analysis choices and data-quality checks
• Variable coding, transformations, rates and logarithms
• Descriptive statistics, distribution plots and subgroup comparisons
• Reproducible project folders, scripts and analysis documentation

Workshop: Participants convert a skills-employment policy brief into a causal diagram, analysis question, variable list and documented data-preparation plan.

Day 2: Regression for policy explanation

• Ordinary least squares regression assumptions and coefficient interpretation
• Multiple regression and control-variable selection
• Dummy variables, interaction terms and reference categories
• Log-linear and log-log specifications for elasticities
• Confidence intervals, p-values and hypothesis tests
• Marginal effects and predicted values for policy scenarios
• Regression tables and coefficient plots for briefing documents

Workshop: Participants estimate employment effects associated with a training programme and produce an annotated regression table with policy interpretation.

Day 3: Diagnostics, robustness and model credibility

• Residual plots and functional-form assessment
• Heteroskedasticity tests and heteroskedasticity-robust standard errors
• Multicollinearity diagnostics using variance inflation factors
• Outliers, leverage and influential-observation analysis
• Serial correlation and clustered standard errors
• Missing-data patterns and defensible treatment choices
• Robustness checks, placebo tests and sensitivity analysis

Workshop: Participants audit a flawed policy regression, correct specification and inference issues, and write a model-risk note for senior reviewers.

Day 4: Evaluating policy interventions

• Fixed-effects and random-effects models for panel data
• Difference-in-differences design and the parallel-trends assumption
• Event-study graphs for dynamic treatment effects
• Instrumental variables, relevance and exclusion restrictions
• Regression discontinuity concepts and threshold-based policies
• Selection bias, omitted variables and threats to identification
• Choosing an evaluation design for available administrative data

Workshop: Participants evaluate a regional business-support scheme using difference-in-differences and prepare a parallel-trends assessment.

Day 5: Forecasting and policy communication

• Time-series components: trend, seasonality and shocks
• Autoregressive models and lag selection
• Forecast construction and out-of-sample accuracy measures
• Scenario analysis and uncertainty ranges for policy planning
• Chart design for ministers, boards and public reports
• Writing findings, caveats and recommendations without overstating causality
• Quality assurance checklists for reproducible policy analysis

Workshop: Participants complete and present a policy evidence pack containing model results, a forecast or scenario chart, limitations and a decision-focused recommendation.

Tools & standards covered

Stata, RStudio, Microsoft Excel, EViews

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