Training on Time Series Forecasting for Economic & Policy Planning

This course offers a hands-on and analytical approach to time series forecasting tailored for economic modeling and policy formulation. It empowers professionals with the skills to interpret historical data, identify trends and cycles, and generate accurate forecasts for evidence-based policymaking. Participants will learn to use statistical and machine learning tools to support macroeconomic planning, budgeting, and socio-economic forecasting.

Duration

5 Days

Who Should Attend

• Economists and Policy Analysts

• Government Planners and Statisticians

• Central Bank and Treasury Officials

• Data Scientists and Research Officers

• Development Agency Economists

• Academics and Graduate Students in Economics

Course Level: Intermediate to Advanced

Organizational Impact

• Improve forecasts for better planning and resource allocation.

• Enable evidence-based policy decisions.

• Increase transparency and stakeholder trust.

Personal Impact

• Gain specialized skills in economics and policy.

• Advance into senior analytical or leadership roles.

• Lead data-driven planning with confidence.

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

• Understand and model time series data in an economic context

• Apply statistical and computational tools for forecasting

• Use time series outputs to inform policy and fiscal planning

• Evaluate forecasting accuracy and improve model performance

Module 1: Fundamentals of Time Series Analysis

• Structure and components of time series (trend, seasonality, noise)

• Stationarity and data transformation techniques

• Visualizing and summarizing economic time series data

• Introduction to software tools: Excel, R, or Python

Module 2: Classical Forecasting Techniques

• Autoregressive (AR), Moving Average (MA), and ARMA models

• Autoregressive Integrated Moving Average (ARIMA) modeling

• Seasonal ARIMA (SARIMA) for cyclical economic data

• Model identification, estimation, and diagnostics

Module 3: Forecasting for Economic and Policy Indicators

• Time series models for GDP, inflation, and unemployment

• Forecasting fiscal revenues and expenditures

• Scenario-based policy simulations using forecasts

• Case study: Forecasting economic growth under uncertainty

Module 4: Advanced and Machine Learning Methods

• Vector Autoregression (VAR) and multivariate time series

• Time series regression and exogenous variable modeling

• Introduction to machine learning models (e.g., LSTM, random forest)

• Comparing model performance using validation techniques

Module 5: Communicating Forecasts for Policy Impact

• Presenting forecasts for policy and budget decisions

• Risk communication and uncertainty quantification

• Creating forecasting dashboards and visuals

• Real-life project: Forecasting and policy brief presentation