Harnessing the Potential of Volatility: Advancing GDP Prediction

TL;DR

This paper introduces a volatility-weighted Lasso method for GDP prediction that improves accuracy and robustness by incorporating macroeconomic shocks, outperforming traditional techniques like Lasso and Adaptive Lasso.

Contribution

The paper proposes a novel volatility-weighted Lasso approach that enhances macroeconomic variable selection and GDP forecasting accuracy under economic shocks.

Findings

Volatility-weighted Lasso outperforms traditional methods in accuracy.

The method is robust to unexpected economic shocks.

It provides a valuable tool for policymakers and analysts.

Abstract

This paper presents a novel machine learning approach to GDP prediction that incorporates volatility as a model weight. The proposed method is specifically designed to identify and select the most relevant macroeconomic variables for accurate GDP prediction, while taking into account unexpected shocks or events that may impact the economy. The proposed method's effectiveness is tested on real-world data and compared to previous techniques used for GDP forecasting, such as Lasso and Adaptive Lasso. The findings show that the Volatility-weighted Lasso method outperforms other methods in terms of accuracy and robustness, providing policymakers and analysts with a valuable tool for making informed decisions in a rapidly changing economic environment. This study demonstrates how data-driven approaches can help us better understand economic fluctuations and support more effective economic policymaking. Keywords: GDP prediction, Lasso, Volatility, Regularization, Macroeconomics Variable Selection, Machine Learning JEL codes: C22, C53, E37.