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

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.

Properties and Use of the Shewhart Method and Its Followers

“…Albers and Kallenberg (2004) conclude that for the Shewhart method the sample size of the estimate has to be very large. Andersson et al (2005) make the same conclusion for other methods.…”

“…Applications in economics, especially the surveillance of business cycles, are treated for example by Andersson et al (2004) and Andersson et al (2005). Predicting the future state of the economy is important both to governments and to businesses.…”

Monitoring System for Detecting Starts and Declines of Influenza Epidemics

“…Using parametric assumption regarding Ii based on the previous pattern, there is always the risk that the current cycle is different from the previous ones, which results in a miss-specification of Ii. In (17) it is demonstrated that a miss-specification of the trend after the turn is not so serious. But if both the trend before and after the turn, i.e.…”

Hidden Markov Model for Predicting the Turning Points of GDP Fluctuation

“…There have some methods to predict the cyclical turning points of economic fluctuation such as function fitting and time series prediction, etc. But these methods emphasize static function dependency or dynamic propagation of time series [2,3] so that the static and dynamic information can not be consistently combined.…”