Real-time Prediction of the Great Recession and the Covid-19 Recession

A series of standard and penalized logistic regression models is employed to model and forecast the Great Recession and the Covid-19 recession in the US. The empirical analysis explores the predictive content of numerous macroeconomic and financial indicators with respect to NBER recession indicator. The predictive ability of the underlying models is evaluated using a set of statistical evaluation metrics. The recessions are scrutinized by closely examining the movement of five most influential predictors that are chosen through automatic variable selections of the Lasso regression, along with the regression coefficients and the predicted recession probabilities. The results strongly support the application of penalized logistic regression models in the area of recession forecasting. Specifically, the analysis indicates that the 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 5 different forecast horizons. The Great Recession is largely predictable, whereas the Covid-19 recession remains unpredictable, given that the Covid-19 pandemic is a real exogenous event. The empirical study reaffirms the traditional role of the term spread as one of the most important recession indicators. The results are validated by constructing via PCA on a set of selected variables a recession indicator that suffers less from publication lags and exhibits a very high association with the NBER recession indicator.