Machine Learning in the Growth-at-Risk Context. A Comparison of Predictors

Inhaltsverzeichnis (Table of Contents)

• Introduction
• The Concept of Growth-at-Risk
  • Literature Overview
  • Quantile Regression
  • LASSO Quantile Methods
    • LASSO Quantile Regression
    • Relaxed LASSO Quantile Regression
    • Adaptive LASSO Quantile Regression
    • Elastic Net Quantile Regression
  • Backtesting
• Empirical Analysis
  • Data
  • In-Sample Analysis
  • Out-of-Sample Analysis
    • Expanding Window Results
    • Rolling Window Results
    • Variable Selection in Out-of-Sample Analysis
  • Discussion
• Conclusion

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This master thesis examines the effectiveness of various machine learning-based variable selection methods in the Growth-at-Risk (GaR) context. It seeks to determine whether utilizing these methods for variable selection can improve the accuracy of GaR predictions compared to traditional methods that rely on aggregated indices.

• Comparison of Machine Learning Methods for Variable Selection in GaR
• Evaluation of the Performance of Machine Learning Models in Out-of-Sample Analysis
• Analysis of the Impact of Variable Selection on Prediction Accuracy
• Investigating the Stability of Variable Selection Across Different Quantiles, Prediction Horizons, and Time Periods
• Exploration of the Potential for Improved Prediction Results in Times of Economic Crisis

Zusammenfassung der Kapitel (Chapter Summaries)

• Introduction: This chapter provides a brief overview of the research question and the methods used in the thesis. It introduces the concept of Growth-at-Risk (GaR) and its relevance in economic forecasting.
• The Concept of Growth-at-Risk: This chapter delves into the theoretical background of GaR, discussing its definition, relevance, and existing literature. It introduces quantile regression as a fundamental tool for estimating GaR and explores various LASSO quantile methods used for variable selection. The chapter concludes with a discussion of backtesting methodologies.
• Empirical Analysis: This chapter presents the empirical study conducted in the thesis. It describes the dataset used, the in-sample analysis results, and the out-of-sample analysis results. The out-of-sample analysis is conducted using both expanding and rolling windows to assess the predictive power of the models over time. The chapter concludes with a discussion of the variable selection process and its impact on the models' performance.

Schlüsselwörter (Keywords)

The primary focus of this thesis revolves around machine learning applications in the context of Growth-at-Risk (GaR). Key concepts include variable selection methods, LASSO quantile regression, Elastic Net, quantile regression, backtesting, expanding window, rolling window, and out-of-sample analysis. The research examines the performance of these methods in predicting GaR, with a particular interest in their effectiveness during periods of economic crisis.