Statistical surveillance of cyclical processes with application to turns in business cycles

Eva Andersson · David Bock · Marianne Frisén

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Advanced Statistical Process Monitoring Forecasting Techniques and Applications Fault Detection and Control Systems

Abstract

On-line monitoring of cyclical processes is studied. An important application is early prediction of the next turn in business cycles by an alarm for a turn in a leading index. Three likelihood-based methods for detection of a turn are compared in detail. One of the methods is based on a hidden Markov model. The two others are based on the theory of statistical surveillance. One of these is free from parametric assumptions of the curve. Evaluations are made of the effect of different specifications of the curve and the transitions. The methods are made comparable by alarm limits, which give the same median time to the first false alarm, but also other approaches for comparability are discussed. Results are given on the expected delay time to a correct alarm, the probability of detection of a turning point within a specified time, and the predictive value of an alarm. Copyright © 2005 John Wiley & Sons, Ltd.

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References

1989A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle 1984Are Economic Time Series Asymmetric over the Business Cycle?1994Business-Cycle Phases and Their Transitional Dynamics 1963On Optimum Methods in Quickest Detection Problems 1995Sequential Changepoint Detection in Quality Control and Dynamical Systems 1966A Comparison of Some Control Chart Procedures 1998Business Cycle Turning Points, A New Coincident Index, and Tests of Duration Dependence Based on a Dynamic Factor Model With Regime Switching 19997. Regime Switching with Time-Varying Transition Probabilities

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2007Properties and Use of the Shewhart Method and Its Followers 2009Monitoring a fraction with easy and reliable settings of the false alarm rate 2011Methods and evaluations for surveillance in industry, business, finance, and public health 2008Statistical Process Control in Asset Management

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