Predictive Modelling In Economics: Forecasting The Future With Data Training Course

Introduction:

In economics, accurate predictions are essential for informed decision-making, policy formulation, and strategic planning. This Predictive Modelling in Economics training course equips you with the cutting-edge tools and techniques to build robust predictive models for economic phenomena. You'll learn how to leverage statistical methods, machine learning algorithms, and econometric principles to forecast key economic variables, analyze trends, and assess the impact of policy interventions. This course empowers you to move beyond descriptive analysis and harness the power of data to anticipate future economic scenarios.

Target Audience:

This course is designed for professionals and researchers in economics and related fields who need to develop and apply predictive models. The target audience includes:

• Economists working in academia, government, and the private sector
• Financial analysts and forecasters
• Data scientists and analysts working with economic data
• Policy analysts and researchers
• PhD students in economics and related fields
• Anyone seeking to improve their economic forecasting and predictive capabilities

Course Objectives:

Upon completion of this Predictive Modelling in Economics training course, participants will be able to:

• Understand the principles and challenges of predictive modelling in economics.
• Apply various statistical and machine learning techniques for economic forecasting, including regression, time series analysis, and classification.
• Develop and evaluate predictive models using appropriate metrics and validation methods.
• Utilize statistical software (e.g., R or Python) for model building and forecasting.
• Forecast key economic variables, such as GDP, inflation, unemployment, and market trends.
• Assess the accuracy and reliability of economic forecasts.
• Incorporate economic theory and domain knowledge into predictive models.
• Communicate forecasting results effectively to stakeholders.
• Understand the limitations and assumptions of different predictive modelling techniques.
• Apply predictive modelling to real-world economic problems and policy analysis.

Duration

10 Days

Course Content

Module 1: Introduction to Predictive Modelling in Economics

• The role of forecasting in economics: Why predict the future?
• Types of economic forecasts: Macroeconomic, microeconomic, financial.
• Principles of predictive modelling: Data, models, evaluation, and deployment.
• Course overview: Structure, learning objectives, software tools (R or Python), and assessment methods.

Module 2: Data Preparation for Predictive Modelling

• Data sources for economic forecasting: Public datasets, private data, surveys.
• Data cleaning and preprocessing: Handling missing values, outliers, and inconsistencies.
• Feature engineering: Creating new variables for improved model performance.
• Data transformation: Scaling, normalization, and other techniques.

Module 3: Regression Models for Economic Forecasting

• Linear regression: Assumptions, estimation, and interpretation.
• Polynomial regression: Modeling non-linear relationships.
• Regularization techniques: Lasso, Ridge, and Elastic Net.
• Model evaluation: R-squared, RMSE, MAE.
• Applications: Forecasting GDP, inflation, and other economic indicators.

Module 4: Time Series Analysis and Forecasting

• Stationarity and autocorrelation: Understanding time series properties.
• ARIMA models: Autoregressive integrated moving average models.
• Seasonal ARIMA models: Handling seasonality in time series.
• Forecasting with ARIMA models: Point forecasts and interval forecasts.
• Applications: Forecasting financial time series, sales data, and other time-dependent variables.

Module 5: Machine Learning for Economic Forecasting I: Regression

• Supervised learning: Regression algorithms.
• Decision trees: Building tree-based regression models.
• Random forests: Combining multiple decision trees.
• Gradient boosting: Boosting model performance.
• Applications: Forecasting economic variables with complex relationships.

Module 6: Machine Learning for Economic Forecasting II: Classification

• Supervised learning: Classification algorithms.
• Logistic regression: Predicting binary outcomes.
• Support vector machines: Finding optimal separating hyperplanes.
• Neural networks: Introduction to deep learning for classification.
• Applications: Predicting economic recessions, credit risk, and market regimes.

Module 7: Model Selection and Evaluation

• Cross-validation: k-fold cross-validation and leave-one-out cross-validation.
• Performance metrics: Choosing the right metrics for different forecasting tasks.
• Bias-variance tradeoff: Balancing model complexity and generalization.
• Model comparison: Information criteria (AIC, BIC).

Module 8: Forecasting Evaluation and Accuracy Assessment

• Evaluating forecast accuracy: In-sample and out-of-sample evaluation.
• Forecast errors: Measuring and analyzing forecast errors.
• Forecast combination: Combining forecasts from multiple models.
• Statistical tests for forecast accuracy: Comparing the performance of different forecasting methods.

Module 9: Nowcasting and Short-Term Forecasting

• Nowcasting: Forecasting the present.
• Mixed-frequency data: Combining data at different frequencies.
• Real-time data: Using timely data for short-term forecasting.
• Applications: Nowcasting GDP, inflation, and other economic indicators.

Module 10: Forecasting with Big Data

• Big data in economics: Opportunities and challenges.
• Data mining and machine learning for big data forecasting.
• Handling large datasets: Distributed computing and cloud platforms.
• Applications: Forecasting with social media data, web data, and other large datasets.

Module 11: Incorporating Economic Theory and Expert Judgment

• The role of economic theory in forecasting.
• Combining statistical forecasts with expert judgment.
• Qualitative forecasting methods: Surveys, Delphi technique.
• Scenario analysis: Developing forecasts under different assumptions.

Module 12: Advanced Topics and Applications

• Time series econometrics: Advanced time series models.
• Dynamic stochastic general equilibrium (DSGE) models for forecasting.
• Forecasting in specific domains: Macroeconomics, finance, microeconomics.
• Ethical considerations in economic forecasting.

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 7 working days before commencement of the training.