TY - GEN
T1 - BUSINESS CYCLE ANALYSIS USING STATISTICAL PROCESS CONTROL
T2 - 43rd International Annual Conference of the American Society for Engineering Management, ASEM 2022
AU - Wendler, Alexander
AU - Beruvides, Mario G.
N1 - Publisher Copyright:
Copyright, American Society for Engineering Management, 2022.
PY - 2022
Y1 - 2022
N2 - The analysis of business cycles has been of interest to economists for more than a century. However, the focus of research has shifted over time - from the ambition of understanding the entire economic process in the beginning to the binary identification of growth and recessionary turning points today. Over the last decades, various statistical models have been utilized for this purpose. Most recently, a new paradigm has been introduced: Modelling business cycles based on the Statistical Process Control (SPC) technique of Self-Starting Cumulative Sum (SS-CUSUM) control charts. In an initial case study, it has been shown that this new approach can reliably reproduce the U.S. National Bureau of Economic Research’s business cycle turning points. The approach further identifies periods of steady state performance with statistically differentiable means and/or standard deviations, as well as patterns of economic activity leading up and away from recessions. The research presented in this paper explores data and methodological related issues that arise when applying the SPC business cycle analysis approach to European economies. Over the course of this study, different sources for macroeconomic data and reference business cycle turning points are evaluated and the implications of the results for the SPC model discussed. This paper is considered the next step in establishing SPC as a paradigm for modelling and analysis of aggregate economic activity that allows engineering managers to better understand the environment, they conduct business in.
AB - The analysis of business cycles has been of interest to economists for more than a century. However, the focus of research has shifted over time - from the ambition of understanding the entire economic process in the beginning to the binary identification of growth and recessionary turning points today. Over the last decades, various statistical models have been utilized for this purpose. Most recently, a new paradigm has been introduced: Modelling business cycles based on the Statistical Process Control (SPC) technique of Self-Starting Cumulative Sum (SS-CUSUM) control charts. In an initial case study, it has been shown that this new approach can reliably reproduce the U.S. National Bureau of Economic Research’s business cycle turning points. The approach further identifies periods of steady state performance with statistically differentiable means and/or standard deviations, as well as patterns of economic activity leading up and away from recessions. The research presented in this paper explores data and methodological related issues that arise when applying the SPC business cycle analysis approach to European economies. Over the course of this study, different sources for macroeconomic data and reference business cycle turning points are evaluated and the implications of the results for the SPC model discussed. This paper is considered the next step in establishing SPC as a paradigm for modelling and analysis of aggregate economic activity that allows engineering managers to better understand the environment, they conduct business in.
KW - Business Cycle Analysis
KW - Cumulative Sum Control Charts
KW - Self-Starting
KW - Statistical Process Control
UR - http://www.scopus.com/inward/record.url?scp=85149172419&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85149172419
T3 - ASEM 43rd International Annual Conference Proceedings
SP - 118
EP - 127
BT - ASEM 43rd International Annual Conference Proceedings
A2 - Natarajan, G.
A2 - Ng, E.H.
A2 - Katina, P.F.
A2 - Zhang, H.
PB - American Society for Engineering Management
Y2 - 5 October 2022 through 8 October 2022
ER -