Introduction

Economic data analysis is essential for policymakers, business leaders, researchers, and analysts who seek to make evidence-based decisions. This program equips participants with the skills to collect, process, interpret, and visualize economic data to uncover trends, evaluate policies, and forecast outcomes. Participants will explore statistical tools, econometric techniques, and modern data visualization methods, applying them to real-world economic challenges.

Target Audience

• Economists and financial analysts

• Policy advisors and researchers

• Business strategy and planning professionals

• Academic and postgraduate students in economics or finance

• Professionals working in government, NGOs, and international organizations

Objectives

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

1. Understand different types and sources of economic data.

2. Apply statistical and econometric techniques for data analysis.

3. Interpret economic indicators to support decision-making.

4. Utilize modern tools for economic data visualization.

5. Develop evidence-based economic reports and forecasts.

Outlines

Day 1:

Fundamentals of Economic Data

• Types of economic data: micro, macro, time series, and panel data

• Key economic indicators (GDP, CPI, unemployment rate, trade balance, etc.)

• Data sources: national statistical agencies, international organizations, and private databases

• Data quality assessment and cleaning techniques

• Case study: Interpreting recent national economic statistics

Day 2:

Statistical Tools for Economic Analysis

• Descriptive statistics: measures of central tendency and dispersion

• Correlation and causation in economics

• Probability distributions relevant to economic data

• Hypothesis testing in economic research

• Practical exercises using Excel or statistical software

Day 3:

Introduction to Econometrics

• Basics of regression analysis and model building

• Simple and multiple linear regression in economic contexts

• Identifying multicollinearity, autocorrelation, and heteroskedasticity

• Application of econometric models to real economic data

• Interpreting regression outputs for policy and business decisions

Day 4:

Economic Forecasting and Modeling

• Time series analysis and trend detection

• Moving averages, exponential smoothing, and ARIMA models

• Using leading and lagging indicators for forecasting

• Scenario analysis and sensitivity testing

• Hands-on exercise: Building a simple GDP growth forecast

Day 5:

Data Visualization and Reporting for Economics

• Principles of effective economic data visualization

• Tools: Excel, Tableau, Power BI for economic dashboards

• Combining charts, maps, and tables for clarity

• Writing economic reports for policymakers and stakeholders

• Final group project: Presenting an economic analysis report