Use of High Dimensional Modeling for automatic variables selection: the best path algorithm

TL;DR

This paper introduces the Best Path algorithm, leveraging Graphical Models for automatic variable selection in large datasets, compatible with various forecasting models, demonstrated through comparison with OLS and LASSO methods.

Contribution

The paper proposes a novel variable selection algorithm based on Graphical Models, suitable for large datasets and adaptable to different forecasting techniques.

Findings

The Best Path algorithm effectively selects variables in large datasets.

Comparison shows the algorithm performs comparably to LASSO.

The method integrates well with OLS and other forecasting models.

Abstract

This paper presents a new algorithm for automatic variables selection. In particular, using the Graphical Models properties it is possible to develop a method that can be used in the contest of large dataset. The advantage of this algorithm is that can be combined with different forecasting models. In this research we have used the OLS method and we have compared the result with the LASSO method.