Econometrics Of High-dimensional Data: Taming The Data Deluge Training Course

Introduction:

The modern world generates data at an unprecedented scale. Economists are increasingly faced with "high-dimensional data" – datasets where the number of variables (features) is much larger than the number of observations. This presents both challenges and opportunities. This Econometrics of High-Dimensional Data training course equips you with the specialized econometric techniques needed to analyze these complex datasets, extract meaningful insights, and avoid the pitfalls of traditional econometric methods. You'll learn how to apply regularization, model selection, and other advanced approaches to tackle high-dimensional problems in economics and related fields.

Target Audience:

This course is designed for professionals and researchers who work with large and complex datasets and need advanced econometric skills to analyze them effectively. The target audience includes:

• Economists working in academia, government, and the private sector
• Data scientists and analysts dealing with high-dimensional data
• Financial analysts and researchers
• Biostatisticians and other researchers working with large datasets
• PhD students in economics, statistics, and related fields
• Anyone seeking to analyze and interpret high-dimensional economic data

Course Objectives:

Upon completion of this Econometrics of High-Dimensional Data training course, participants will be able to:

• Understand the challenges and opportunities presented by high-dimensional data in econometrics.
• Apply regularization techniques, such as Lasso, Ridge regression, and Elastic Net, to high-dimensional regression problems.
• Perform model selection and variable selection in high-dimensional settings.
• Utilize penalized regression methods for model estimation and inference.
• Implement dimension reduction techniques, such as Principal Component Analysis (PCA), for high-dimensional data.
• Analyze high-dimensional panel data and time series data.
• Use statistical software (e.g., R or Python) for high-dimensional econometric analysis.
• Interpret and communicate results from high-dimensional econometric models.
• Understand the theoretical foundations of high-dimensional econometrics.
• Apply high-dimensional econometric methods to real-world problems in economics, finance, and other fields.

Duration

10 Days

Course Content

Module 1: Introduction to High-Dimensional Data in Econometrics

• The "curse of dimensionality": Challenges and opportunities with many variables.
• Why traditional econometric methods may fail with high-dimensional data.
• Examples of high-dimensional data in economics and related fields.
• Course overview: Structure, learning objectives, software tools (R or Python), and assessment methods.

Module 2: Linear Regression and the OLS Limitations

• Review of ordinary least squares (OLS) regression: Assumptions and limitations.
• Overfitting and multicollinearity in high-dimensional settings.
• The bias-variance tradeoff.
• Introduction to regularization as a solution.

Module 3: Regularization Techniques: Lasso

• Lasso regression: Shrinking coefficients and variable selection.
• Geometric interpretation of Lasso.
• Tuning parameter selection: Cross-validation and other methods.
• Applications of Lasso in economics.

Module 4: Regularization Techniques: Ridge Regression

• Ridge regression: Shrinking coefficients without variable selection.
• Geometric interpretation of Ridge regression.
• Tuning parameter selection for Ridge regression.
• Comparing Lasso and Ridge regression.

Module 5: Regularization Techniques: Elastic Net

• Elastic Net: Combining Lasso and Ridge regression.
• Advantages of Elastic Net.
• Tuning parameter selection for Elastic Net.
• Applications and examples.

Module 6: Model Selection and Variable Selection

• The importance of model selection in high dimensions.
• Information criteria: AIC, BIC.
• Cross-validation: k-fold cross-validation and leave-one-out cross-validation.
• Variable selection methods: Forward selection, backward elimination.

Module 7: Penalized Regression Methods

• General framework for penalized regression.
• Different penalty functions: L1, L2, and others.
• Computational aspects of penalized regression.
• Inference after model selection.

Module 8: Dimension Reduction Techniques: Principal Component Analysis (PCA)

• Principal component analysis: Reducing dimensionality by finding principal components.
• Eigenvalues and eigenvectors: Understanding the mathematics of PCA.
• Applications of PCA in economics.
• Limitations of PCA.

Module 9: High-Dimensional Panel Data

• Panel data structures: Balanced and unbalanced panels.
• Fixed effects and random effects models in high dimensions.
• Regularization techniques for panel data.
• Applications: Analyzing economic growth, firm performance with many variables.

Module 10: High-Dimensional Time Series

• Time series analysis with many variables.
• Vector autoregressive (VAR) models in high dimensions.
• Regularization techniques for time series models.
• Applications: Forecasting macroeconomic variables, financial time series.

Module 11: Inference in High Dimensions

• Challenges of statistical inference with many variables.
• False discovery rate control.
• Multiple testing problem.
• Bootstrapping and other resampling methods.

Module 12: Advanced Topics and Applications

• Factor models: Modeling latent factors in high-dimensional data.
• Sparse regression: Dealing with ultra-high dimensional data.
• Applications in specific fields: Finance, macroeconomics, microeconomics.
• Ethical considerations and responsible use of high-dimensional data.

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.