Using statistical process monitoring to identify us business cycle change points and turning points

Abstract

ABSTRACT The Business Cycle paradigm of Mitchell and Burns has evolved from their original goal of understanding the entire economic process to the binary identification of growth and recessionary Turning Points. We propose a new paradigm for modelling the Business Cycle based on the Statistical Process Monitoring technique of Self-Starting Cumulative Sum (SSCUSUM) control charts. The SSCUSUM charts provide continuous characterization of aggregate economic activity through the identification of changes in the mean or standard deviation of economic indicators. A case study is conducted using real GDP % growth between 1965 and 2020 which shows that SSCUSUM charts: identify periods of steady state performance with statistically differentiable means and/or standard deviations, reliably reproduce the National Bureau of Economic Research Business Cycle Turning Points, identify patterns of economic activity leading up to and away from recessions, and identify twice the information on economic performance as the current bivariate approach. Over the study period, the SSCUSUM method identifies 42 changes in the mean or standard deviation of real GDP % growth, while the NBER TPs identify 8 peaks and 7 troughs.