In today's complex global economy, accurate inflation forecasting is more critical than ever for central banks, financial institutions, and business leaders. It serves as a cornerstone for effective monetary policy, sound investment strategies, and strategic business planning. Traditional forecasting methods, while foundational, often struggle to capture the intricate, non-linear relationships and high-frequency data that characterize modern economies. This makes advanced econometric techniques indispensable for building more robust and reliable models that can peer through the economic noise and provide a clearer signal of future price dynamics.

This program provides a rigorous and practical guide to the cutting-edge techniques and models used to forecast inflation in a data-rich environment. Participants will gain a deep understanding of everything from advanced time series analysis and the Phillips Curve to the application of machine learning and big data in economic forecasting. The course is designed to bridge the gap between academic theory and the operational realities of a central bank research department or a financial firm's trading desk. By combining robust theory with hands-on applications, attendees will be equipped to tackle the challenges of a constantly evolving economic landscape and produce forecasts that are not only accurate but also transparent and reliable.

Duration: 10 days

Target Audience:

• Central Bank Economists and Researchers
• Financial Analysts and Portfolio Managers
• Macroeconomic Forecasters and Consultants
• Data Scientists with an interest in Economics
• Graduate Students in Economics and Finance
• Government and Policy Analysts
• Banking and Risk Management Professionals
• Corporate Strategy and Planning Staff
• Investment Firm Researchers
• Academics and Educators

Objectives:

• Master the core theoretical and empirical models of inflation.
• Learn to apply advanced time series models to inflation data.
• Understand the role of the Phillips Curve in a modern context.
• Grasp the complexities of forecasting with unobserved components.
• Develop proficiency in building and interpreting Vector Autoregressive (VAR) models.
• Explore the use of big data and machine learning in forecasting.
• Learn about robust methods for forecast evaluation and model comparison.
• Identify the critical role of inflation expectations and survey data.
• Develop skills in translating model output into policy recommendations.
• Formulate strategies for forecasting in a dynamic and uncertain environment.

Course Modules:

Module 1: Foundations of Inflation Forecasting

• Defining inflation and its measurement
• The theories of inflation (e.g., quantity theory, New Keynesian Phillips Curve)
• The role of inflation expectations and central bank communication
• The challenges of forecasting in real-time
• The costs and benefits of accurate forecasting

Module 2: Univariate Time Series Analysis

• Stationarity, unit roots, and autoregressive models
• The use of ARIMA and SARIMA models
• The role of a "clear and focused research question"
• Nowcasting and the use of high-frequency data
• Identifying structural breaks in time series data

Module 3: Advanced Time Series Models

• The use of Vector Autoregressive (VAR) models
• The importance of a "risk and mitigation" plan
• Impulse response functions and variance decompositions
• The Vector Error Correction Model (VECM) for cointegrated series
• Forecasting with dynamic factor models

Module 4: The Phillips Curve Revisited

• The New Keynesian Phillips Curve
• The role of slack and marginal costs in inflation
• The importance of a simple scorecard and a dashboard
• The challenges of measuring inflation expectations
• The role of a "data story map"

Module 5: Unobserved Components Models

• The concept of unobserved trend and cycle
• The use of state-space models and the Kalman filter
• The importance of a "clear and consistent reporting style"
• Estimating potential output and the output gap
• Forecasting inflation with a non-linear Phillips Curve

Module 6: Inflation Expectations and Surveys

• The different types of inflation expectations (rational, adaptive)
• The use of consumer and business surveys (e.g., Survey of Professional Forecasters)
• The role of a program's theory of change
• Extracting information from financial market data (e.g., TIPS spreads)
• The central bank's role in anchoring expectations

Module 7: Forecasting with Big Data

• The use of big data in economic forecasting
• The importance of a "stakeholder analysis"
• Sourcing and cleaning high-frequency data (e.g., web-scraped prices)
• The role of a clear and compelling KPI
• The use of sentiment analysis and natural language processing (NLP)

Module 8: Machine Learning for Inflation Forecasting

• Introduction to machine learning (ML) models
• The difference between ML and traditional econometrics
• The role of a "risk and mitigation" plan
• The use of random forests and gradient boosting
• Forecasting with neural networks and deep learning

Module 9: Model Evaluation and Comparison

• The different metrics for forecast accuracy (e.g., RMSE, MAE)
• The importance of a simple scorecard and a dashboard
• The use of out-of-sample forecasting
• The Diebold-Mariano test for forecast superiority
• The challenges of forecast uncertainty

Module 10: Fiscal and Monetary Policy Effects

• The impact of fiscal policy on inflation
• The importance of a "data story map"
• The relationship between public debt and inflation
• The role of unconventional monetary policies
• The challenges of high public debt

Module 11: Forecasting in an Open Economy

• The role of exchange rates and import prices
• The impact of global supply chains on inflation
• The importance of a "clear and consistent reporting style"
• The use of global factors in inflation models
• The role of international commodity prices

Module 12: Communication and Transparency

• The role of a program's theory of change
• Communicating inflation forecasts to the public and markets
• The use of fan charts to illustrate forecast uncertainty
• The importance of a "stakeholder analysis"
• The role of a clear and compelling KPI

Module 13: Nowcasting and Real-Time Data

• The concept of nowcasting vs. forecasting
• The use of real-time data for forecasting
• The role of a "risk and mitigation" plan
• The importance of a simple scorecard and a dashboard
• The challenges of data management

Module 14: Case Studies

• Case study: Forecasting during a period of high inflation
• Case study: The impact of a global supply chain shock
• Case study: The challenges of forecasting with a new monetary policy tool
• Lessons from central banks around the world
• The role of a "data story map"

Module 15: Software and Tools

• The use of R and Python for econometric analysis
• The importance of a "clear and consistent reporting style"
• The use of specialized software (e.g., EViews, Stata)
• The role of a "program's theory of change"
• Best practices for coding and reproducibility

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.