  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.
These two recessions are scrutinized by closely examining the movement of five
chosen predictors, their regression coefficients, and the predicted
probabilities of recession. 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 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 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 results are validated by
constructing via principal component analysis (PCA) on a set of selected
variables a recession indicator that suffers less from publication lags and
exhibits a very high correlation with the NBER recession indicator.

Econometrics