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

Author:

Seulki Chung

Keyword:

Economics, Econometrics, Econometrics (econ.EM)

journal:

date:

2023-10-11 16:00:00

Abstract

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.

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