Forecasting Unemployment Recession Rates: A Logistic Regression Approach Utilizing Comprehensive Economic Indicators

This paper presents a predictive model using logistic regression to forecast unemployment recession rates, utilizing a range of variables such as GDP, inflation rates, employment rates, and trade activities. The model aims to provide accurate predictions of recession occurrence, utilizing historical data and rigorous cross-validation techniques. The model's significance lies in its potential applications in financial prudence and risk management, enabling job seekers to plan their personal finances and career trajectories, while companies can integrate these insights into strategic planning and supply chain optimization. The model's interpretability provides actionable insights by highlighting the significance and direction of each variable's impact on the predicted recession rate. The paper contributes to a deeper understanding of economic recessions and empowers stakeholders to navigate challenges with resilience and confidence. The model's deployment offers a practical tool for individuals and organizations to make informed choices during economic uncertainties, ultimately fostering prudent and sustainable economic strategies.