Adaptive Modeling Procedure Selection by Data Perturbation.

Area of Science:

• Econometrics
• Statistical Modeling
• Machine Learning

Background:

• High-dimensional data presents challenges in business and economics.
• Accurately assessing modeling uncertainty from model selection and parameter estimation is crucial.
• Existing data perturbation methods lack adaptability in determining optimal perturbation size.

Purpose of the Study:

• To develop a data-adaptive model selection method that accounts for modeling uncertainty.
• To derive the optimal data perturbation size adaptable to data and model characteristics.
• To enhance prediction accuracy in high-dimensional economic and business contexts.

Main Methods:

• Developed an adaptive data-perturbation method for linear regression.
• Derived an optimal perturbation size that adapts to data and model space.
• Utilized theoretical and numerical analysis to validate the method's performance.
• Applied the method to commodity market data for price forecasting.

Main Results:

• The adaptive data-perturbation method demonstrates superior performance across different situations compared to nonadaptive methods.
• The proposed data-adaptive model selection method achieves consistent model selection and optimal prediction.
• The method shows improved price forecasting accuracy on real commodity market data.

Conclusions:

• The data-adaptive model selection method effectively handles modeling uncertainty in high-dimensional settings.
• This approach offers a robust solution for improving predictive performance in economics and business analytics.
• The method's adaptability makes it a valuable tool for real-world forecasting applications.