High-Dimensional Econometrics and LASSO Training Course
Introduction
In the modern era of Big Data, economists are increasingly confronted with datasets where the number of variables (p) often far exceeds the number of observations (n), a scenario known as high-dimensionality. Traditional econometric techniques falter in such environments, leading to issues like multicollinearity, overfitting, and unreliable inference. This intensive 5-day training course is expertly crafted to equip economists, quantitative analysts, and researchers with the cutting-edge High-Dimensional Econometrics tools, with a particular focus on the powerful LASSO (Least Absolute Shrinkage and Selection Operator) method. You will gain a deep understanding of how regularization techniques provide robust solutions for variable selection, parameter estimation, and improved prediction accuracy in high-dimensional settings, enabling you to extract meaningful insights from vast and complex economic datasets.
Duration: 5 Days
Target Audience:
- Economists and econometricians
- Data scientists in finance and economics
- Researchers and academics dealing with large datasets
- Graduate students (Master's and Ph.D.) in quantitative economics
- Financial analysts and quants
- Policy researchers
- Business intelligence professionals
- Anyone interested in advanced predictive modeling
Objectives:
- To understand the theoretical foundations and challenges of high-dimensional econometrics.
- To master the principles and applications of LASSO regression for variable selection and regularization.
- To acquire practical skills in implementing LASSO and related techniques using statistical software.
- To learn how to select optimal tuning parameters for penalized regression models.
- To competently interpret and communicate the results of high-dimensional econometric analyses.
- To explore the benefits of LASSO for improved out-of-sample prediction in economic forecasting.
- To understand how to apply regularization methods to various econometric models beyond linear regression.
- To address issues of multicollinearity, overfitting, and bias-variance trade-off in high dimensions.
- To compare and contrast LASSO with other regularization techniques like Ridge and Elastic Net.
- To critically evaluate and apply high-dimensional methods to real-world economic problems.
Course Modules:
- Introduction to High-Dimensional Econometrics
- The rise of Big Data in economics and the "p >> n" problem.
- Limitations of classical econometric methods in high dimensions.
- The bias-variance trade-off in predictive modeling.
- The concept of sparsity in economic models.
- Overview of regularization as a solution to high-dimensionality.
- Review of Linear Regression and its High-Dimensional Challenges
- Assumptions and properties of Ordinary Least Squares (OLS).
- Consequences of high dimensionality for OLS (multicollinearity, overfitting).
- Introduction to the concept of model complexity.
- The need for variable selection and shrinkage.
- Data preprocessing and standardization for high-dimensional data.
- LASSO Regression: Theory and Application
- The L1 penalty and its role in LASSO.
- How LASSO performs simultaneous variable selection and shrinkage.
- Geometric interpretation of the LASSO penalty.
- Mathematical formulation and optimization of the LASSO problem.
- Applications of LASSO in various economic contexts.
- Tuning Parameter Selection for LASSO
- The critical role of the regularization parameter (λ).
- Cross-validation (K-fold, Leave-One-Out) for optimal λ
- Information criteria for model selection (AIC, BIC) in a high-dimensional context.
- Practical considerations for choosing λ in economic applications.
- Software implementation of cross-validation for LASSO.
- Beyond LASSO: Ridge Regression and Elastic Net
- Ridge Regression: The L2 penalty and shrinkage without variable selection.
- Geometric interpretation of Ridge Regression.
- Elastic Net: Combining L1 and L2 penalties for variable selection and grouping.
- Advantages and disadvantages of LASSO, Ridge, and Elastic Net.
- Selecting the appropriate regularization method for different economic problems.
- Inference and Post-Selection Inference in High Dimensions
- Challenges of statistical inference after variable selection.
- Bias introduced by shrinkage in LASSO.
- Introduction to post-LASSO inference methods (e.g., desparsified LASSO).
- Constructing valid confidence intervals in high-dimensional models.
- Hypothesis testing in regularized regression frameworks.
- Applications of High-Dimensional Econometrics
- Macroeconomic forecasting with many predictors.
- Financial econometrics: Portfolio selection and risk management.
- Microeconometrics: Treatment effects with many controls.
- Analyzing large-scale panel data in economics.
- Machine learning approaches for economic prediction and inference.
- Practical Implementation and Advanced Topics
- Hands-on exercises using specialized R or Python packages for high-dimensional econometrics (e.g., glmnet, hdm, lars).
- Data visualization and interpretation of LASSO paths.
- Addressing computational challenges in high-dimensional models.
- Introduction to group LASSO and sparse group LASSO.
- Future directions and emerging methods in high-dimensional econometrics.
CERTIFICATION
- Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate
TRAINING VENUE
- Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.
AIRPORT PICK UP AND ACCOMMODATION
- Airport pick up and accommodation is arranged upon request
TERMS OF PAYMENT
Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com
For More Details call: +254-114-087-180
High-dimensional Econometrics And Lasso Training Course in Kenya
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