Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 4, 2022 erstellt
Beschreibung:
A series of standard and penalized logistic regression models is used for modeling and forecasting Great Recession and COVID-19 recession in the US. These two recessions are scrutinized by taking a close look at the movement of five chosen predictors themselves and their regression coefficients along with predicted recession probabilities. The empirical analysis explores the predictive content of numerous macroeconomic and financial indicators. The predictive ability of the underlying models is evaluated using a set of statistical evaluation metrics. The results strongly support the application of penalized logistic regression models in the area of recession prediction. Specifically, the analysis indicates that a mixed usage of different penalized logistic regression models over different forecast horizons largely outperform standard logistic regression models in the prediction of Great recession in the US, as they achieve higher predictive accuracy across 4 different forecast horizons. Great Recession is largely predictable, whereas COVID-19 recession remains unpredictable as the COVID-19 pandemic is a real exogenous event